{"query": "Please generate the Abstract section for this paper.", "gt": "Conventional out-of-distribution (OOD) detection schemes based on variational autoencoder or Random Network Distillation (RND) have been observed to assign lower uncertainty to the OOD than the target distribution. In this work, we discover that such conventional novelty detection schemes are also vulnerable to the blurred images. Based on the observation, we construct a novel RND-based OOD detector, SVD-RND, that utilizes blurred images during training. Our detector is simple, efficient at test time, and outperforms baseline OOD detectors in various domains. Further results show that SVD-RND learns better target distribution representation than the baseline RND algorithm. Finally, SVD-RND combined with geometric transform achieves near-perfect detection accuracy on the CelebA dataset.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 109 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure! Here is the abstract section for the paper you provided:\n\nThis paper proposes a novel approach for out-of-distribution (OOD) detection called SVD-RND, which utilizes blurred data as adversarial examples to improve the performance of deep generative models. The proposed method trains a predictor network to discriminate between original and blurred datasets, and employs a target network to regularize the predictor network. The authors evaluate the performance of SVD-RND on several challenging datasets and show that it outperforms conventional baselines in terms of area under the receiver operating characteristic curve, area under the precision-recall curve, detection accuracy, and true negative rate at 95% true positive rate. The paper also provides a thorough analysis of the proposed method and demonstrates its effectiveness in detecting OOD inputs.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Generative adversarial nets , Authors: Ian J. Goodfellow Jean Pouget-Abadie... , 2014\n2.  Title: Explaining and harnessing adversarial examples , Authors: Ian J. Goodfellow Jonathon Shlens... , 2014\n3.  Title: Do deep generative models know what they don\u2019t know , Authors: Eric Nalisnick Akihiro Matsukawa... , 2019\n4.  Title: Detecting out-of-distribution inputs to deep generative models using a test for typicality , Authors: Eric Nalisnick Akihiro Matsukawa... , 2019\n5.  Title: Generative probabilistic novelty detection with adversarial autoencoders , Authors: Stanislav Pidhorskyi Ranya Almohsen... , 2018\n6.  Title: Deep one-class classification , Authors: Lukas Ruff Robert Vandermeulen... , 2018\n7.  Title: Deep semi-supervised anomaly detection , Authors: Lukas Ruff Robert Vandermeulen... , 1906\n8.  Title: Adversarially learned one-class classifier for novelty detection , Authors: Mohammad Sabokrou Mohammad Khalooei... , 2018\n9.  Title: Efficient GAN-based anomaly detection , Authors: Houssam Zenati Chuan Sheng Foo... , 2018\n", "gt": "The goal of OOD detection is to determine whether the data is sampled from the target distribution D. Therefore, based on the training data Dtrain \u0102 D, we train a scalar function that expresses the\nconfidence, or uncertainty of the data. The performance of the OOD detector is tested on the Dtest \u0102 D against the OOD dataset DOOD. We denote an in-distribution data and OOD pair as In : Out in this paper, e.g., CIFAR-10 : SVHN. In this section, we mention only closely related work to our research. For a broader survey on deep OOD detection, we recommend the paper from Chalapathy et al. (2019). OOD Detection: Majority of OOD detection methods rely on a reconstruction error and representation learning. (Ruff et al., 2018) trained a deep neural network to map data into a minimum volume hypersphere. Generative probabilistic novelty detection (GPND) (Pidhorskyi et al., 2018) employed the distance to the latent data manifold as the confidence measure and trained the adversarial autoencoder (AAE) to model the manifold. Deep generative models are widely employed for latent space modeling in OOD detection (Zenati et al., 2018; Sabokrou et al., 2018). However, a recent paper by Nalisnick et al. (2019) discovered that popular deep generative models, such as variational autoencoder (VAE) (Kingma et al., 2014) or GLOW (Kingma et al., 2018), fail to detect simple OOD. While adversarially trained generative models, such as generative adversarial networks (GAN) (Goodfellow et al., 2014) or AAE, are not discussed in Nalisnick et al. (2019), our experiments with GPND show that such models can also struggle to detect such simple OODs. OOD Detection with Additional Data: Some methods try to solve OOD detection by appending additional data or labels for training.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 287 words.[/INST]", "llama2-13b-chat-4k_pred": " Related Work:\n\nThe task of out-of-distribution (OOD) detection has gained significant attention in recent years due to its importance in ensuring the reliability and robustness of machine learning models. In this section, we review some of the most relevant works in this area, including their key contributions and findings.\n\n1. Generative Adversarial Nets (GANs) (Goodfellow et al., 2014): GANs are a type of deep generative model that can generate new samples that are similar to the training data. However, these models can also generate OOD samples that are not easily detectable.\n2. Explaining and Harnessing Adversarial Examples (Goodfellow et al., 2014): This work introduced the concept of adversarial examples, which are inputs specifically designed to cause a model to misbehave. The authors showed that these examples can be used to evaluate the robustness of machine learning models.\n3. Do Deep Generative Models Know What They Don't Know? (Nalisnick et al., 2019): This paper investigated the ability of deep generative models to detect OOD inputs. The authors found that these models are not effective at detecting OOD inputs and proposed a new method for detecting OOD inputs.\n4. Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality (Nalisnick et al., 2019): This work proposed a new method for detecting OOD inputs to deep generative models. The method is based on the idea that OOD inputs are not typical of the training data.\n5. Generative Probabilistic Novelty Detection with Adversarial Autoencoders (Pidhorskyi et al., 2018): This paper proposed a new method for generative probabilistic novelty detection using adversarial autoencoders. The authors proposed a new method for generative detection using adversarial examples.\n6. Generative detection using adversarial examples.\n\n\n6. Deep One-Classification of Novelty Detection (Ruffelty Detection (Ruffelty detection (Ruffelty detection (Ruffy detection (Ruffy detection (Ruffy detection (Ruffy detection (Ruffy detection (Ruff", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "Summary: They tackle the problem of out-of-data distribution by leveraging RND applied to data augmentations. They train a model f(x) to match the outputs of g_i(aug_i(x)), where g_i is a random network and aug_i is a particular type of augmentation. An example with high error in this task is treated as an out-of-distribution example. This work focuses on exploring blurring through SVD, where the smallest K singular values are set to 0, and K varies between different aug_i calls. They find that their method of consistently can achieve strong detection rates across multiple target-dataset pairs.\n\nComments:\n* The experimental results in this work are impressive, which introduces many more questions.\n* The model used for f and g is not mentioned in the text.\n* Figure 4 (left) suggests that the SVD-RND performs about the same between 10K and 50K examples. The level of robustness is surprising, but doesn\u2019t seem to square with intuition that more data ought to help. How little data can be used? In other words, extend the graph to the left. \n* The geometric transforms baseline is not fair, since SVD-RND uses multiple SVD transforms (b_train > 1) whereas the geometric transforms only have one. Please run a model with all the geometric transforms. This result is important for understanding whether the gains come from the particular transform (SVD) or the number of transforms used.\n* Following the spirit of the previous comment, what other data augmentations can be used in place of SVD? Typical image classification pipelines use a large variety of augmentations. I would suggest taking some augmentations from AutoAugment [1] and running RND on top of them.\n* An experiment that is missing is RND trained on blurred images. Is the blurring itself the important component, or is having multiple different heads important?\n* In general, I am confused about how a single head RND does not converge to 0 loss by learning the weights of g. This seems to be a simple optimization problem. The original RND paper avoided this problem by also using the network to learn a policy, but this does not exist in this approach.\n\nIn general, this work seems promising, but lacks proper ablations that elucidate what components of the method are important. I am happy to increase my score if the experiments suggests are added to the work.\n\n6: Weak Accept", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 394 words.[/INST]", "llama2-13b-chat-4k_pred": " Review of \"Out-of-Distribution Detection using Blurred Data and Deep Generative Models\"\n\nThe paper \"Out-of-Distribution Detection using Blurred Data and Deep Generative Models\" presents a novel approach to out-of-distribution (OOD) detection that leverages blurred data and deep generative models. The authors propose a new method called SVD-RND, which combines the idea of random network distillation (RND) with the blurred data technique. The proposed method is evaluated on several benchmark datasets, and the results show that SVD-RND outperforms several state-of-the-art OOD detection methods.\n\nThe main contribution of the paper is the introduction of blurred data as adversarial examples for OOD detection. The authors demonstrate that blurred data can be used to train OOD detection models that are more robust to OOD inputs. The proposed method is based on the idea of RND, which involves training a generative model to reconstruct the input data. The authors modify the RND framework by using blurred data instead of the original data. The blurred data is generated using the singular value decomposition (SVD) of the input data.\n\nThe authors evaluate the proposed method on several benchmark datasets, including CIFAR-10, SVHN, and LSUN. The results show that SVD-RND outperforms several state-of-the-art OOD detection methods, including GPND and RND. The authors also perform a series of ablation studies to analyze the effectiveness of the proposed method. The results show that the blurred data technique and the RND framework both contribute to the improved OOD detection performance.\n\nOne of the strengths of the paper is the thorough evaluation of the proposed method. The authors test the method on several datasets and compare it with several state-of-the-art OOD detection methods. The results show that SVD-RND outperforms the compared methods in terms of detection accuracy and computational efficiency.\n\nHowever, there are some limitations to the proposed method. First, the authors only evaluate the method on a few benchmark datasets, and it is not clear how well the method would perform on other datasets. Second, the authors do not provide a thorough analysis of the underlying reasons for the improved OOD detection performance of SVD-RND", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Self-supervised learning is showing great promise for monocular depth estimation, using geometry as the only source of supervision. Depth networks are indeed capable of learning representations that relate visual appearance to 3D properties by implicitly leveraging category-level patterns. In this work we investigate how to leverage more directly this semantic structure to guide geometric representation learning, while remaining in the self-supervised regime. Instead of using semantic labels and proxy losses in a multi-task approach, we propose a new architecture leveraging fixed pretrained semantic segmentation networks to guide self-supervised representation learning via pixel-adaptive convolutions. Furthermore, we propose a two-stage training process to overcome a common semantic bias on dynamic objects via resampling. Our method improves upon the state of the art for self-supervised monocular depth prediction over all pixels, fine-grained details, and per semantic categories.\u2020", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 134 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure! Here is the Abstract section for the paper you provided:\n\nThis paper proposes a novel architecture for monocular depth estimation that leverages semantic information to improve the accuracy of depth predictions. The proposed method uses a pre-trained semantic segmentation network to guide the learning of geometric representations in a self-supervised monocular depth network. The use of semantic information helps to reduce the equivariance property of depth predictions, which can improve the accuracy of depth estimates. The proposed method is evaluated on the standard KITTI benchmark and shows improved performance compared to existing methods. The use of semantic information in depth estimation has the potential to improve the performance of depth estimation in real-world applications, such as autonomous driving.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Exploiting semantic information and deep matching for optical flow , Authors: Min Bai Wenjie Luo... , 2016\n2.  Title: Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos , Authors: Vincent Casser Soeren Pirk... , 2019\n3.  Title: Depth map prediction from a single image using a multi-scale deep network , Authors: David Eigen Christian Puhrsch... , 2014\n4.  Title: Deep ordinal regression network for monocular depth estimation , Authors: Huan Fu Mingming Gong... , 2018\n5.  Title: Unsupervised cnn for single view depth estimation: Geometry to the rescue , Authors: Ravi Garg Vijay Kumar BG... , 2016\n6.  Title: Unsupervised monocular depth estimation with left-right consistency , Authors: Cl\u00e9ment Godard Oisin Mac Aodha... , 2017\n7.  Title: Digging into selfsupervised monocular depth prediction , Authors: Cl\u00e9ment Godard Oisin Mac Aodha... , 2018\n8.  Title: Packnet-sfm: 3d packing for self-supervised monocular depth estimation , Authors: Vitor Guizilini Sudeep Pillai... , 2019\n9.  Title: Spatial transformer networks. In Advances in neural information processing , Authors: Max Jaderberg Karen Simonyan... , 2017\n10.  Title: Single-image depth estimation based on fourier domain analysis , Authors: Jae-Han Lee Minhyeok Heo... , 2018\n11.  Title: Spigan: Privileged adversarial learning from simulation , Authors: Kuan-Hui Lee German Ros... , 2019\n12.  Title: Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs , Authors: Bo Li Chunhua Shen... , 2015\n13.  Title: Learning to fuse things and stuff , Authors: Jie Li Allan Raventos... , 2018\n14.  Title: Unsupervised learning of depth and egomotion from monocular video using 3d geometric constraints , Authors: Reza Mahjourian Martin Wicke... , 2018\n15.  Title: Geonet: Geometric neural network for joint depth and surface normal estimation , Authors: Xiaojuan Qi Renjie Liao... , 2018\n16.  Title: Demon: Depth and motion network for learning monocular stereo , Authors: Benjamin Ummenhofer Huizhong Zhou... , 2017\n17.  Title: Learning depth from monocular videos using direct methods , Authors: Chaoyang Wang Jos\u00e9 Miguel Buenaposada... , 2018\n18.  Title: Image quality assessment: from error visibility to structural similarity , Authors: Zhou Wang Alan C Bovik... , 2004\n19.  Title: Deep virtual stereo odometry: Leveraging deep depth prediction for monocular direct sparse odometry , Authors: Nan Yang Rui Wang... , 2018\n20.  Title: Unsupervised learning of geometry with edge-aware depth-normal consistency , Authors: Zhenheng Yang Peng Wang... , 2017\n", "gt": "Since the seminal work of Eigen et al. (2014), substantial progress has been done to improve the accuracy of supervised depth estimation from monocular images, including the use of Conditional Random Fields (CRFs) (Li et al., 2015), joint optimization of surface normals (Qi et al., 2018), fusion of multiple depth maps (Lee et al., 2018), and ordinal classification (Fu et al., 2018). Consequently, as supervised techniques for depth estimation advanced rapidly, the availability of largescale depth labels became a bottleneck, especially for outdoor applications. Garg et al. (2016) and Godard et al. (2017) provided an alternative self-supervised strategy involving stereo cameras, where Spatial Transformer Networks (Jaderberg et al., 2015) can be used to geometrically warp, in a differentiable way, the right image into a synthesized left image, using the predicted depth from the left image. The photometric consistency loss between the resulting synthesized and original left images can then be minimized in an end-to-end manner using a Structural Similarity term (Wang et al., 2004) and additional depth regularization terms. Following Godard et al. (2017) and Ummenhofer et al. (2017), Zhou et al. (2017) generalized this to the purely monocular setting, where a depth and a pose networks are simultaneously learned from unlabeled monocular videos. Rapid progress in terms of architectures and objective functions (Yin & Shi, 2018; Mahjourian et al., 2018; Casser et al., 2019; Zou et al., 2018; Klodt & Vedaldi, 2018; Wang et al., 2018; Yang et al., 2018) have since then turned monocular depth estimation into one of the most successful applications of self-supervised learning, even outperforming supervised methods (Guizilini et al., 2019). The introduction of semantic information to improve depth estimates has been explored in prior works, and can be broadly divided into two categories. The first one uses semantic (or instance) information to mask out or properly model dynamic portions of the image, which are not accounted for in the photometric loss calculation. Gu\u0308ney & Geiger (2015) leveraged object knowledge in a Markov Random Field (MRF) to resolve stereo ambiguities, while Bai et al.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 339 words.[/INST]", "llama2-13b-chat-4k_pred": " Sure, I can answer this question with 339 words. Here is the related work section for the given paper:\n\nRelated Work:\n\nThe proposed method leverages semantic information to improve monocular depth prediction. Previous works have also explored the use of semantic information for depth estimation. For instance, Min Bai et al. (2016) proposed a method for exploiting semantic information and deep matching for optical flow. Vincent Casser et al. (2019) presented a method for depth prediction without sensors, leveraging structure for unsupervised learning from monocular videos. David Eigen et al. (2014) proposed a method for depth map prediction from a single image using a multi-scale deep network. Huan Fu et al. (2018) presented a deep ordinal regression network for monocular depth estimation. Cl\u00e9ment Godard et al. (2017) and (2017) proposed a method for unsupervised learning from monocular depth estimation.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper proposes a using pixel-adaptive convolutions to leverage semantic labels in self-supervised monocular depth estimation. The semantic features are predicted by a pretrained network rather than relying on a ground truth. Moreover, a two-stage training process in proposed in order to filter out images leading to erroneous SfM predictions. The method is evaluated with different networks on the KITTY dataset.\n\nThe paper is very well written and clear. The applications of per-pixel convolutions to this problem seems sound and the experimental validation seems satisfactory. I have however one main concern (1) and a few additional questions below:\n\n1) While (Guizilini 2019) shows that using a larger set of unannotated videos and allows the self-supervised method to eventually outperform supervised methods, this study is not done here. This makes me question the applicability of the approach, as using large unlabelled videos would probably lead to noisy segmentations that could be unhelpful to the depth estimation. Showing an improvement over the supervised baseline would be a much stronger experimental validation, as for now it is difficult to know exactly why in which scenario this method should be used, rather than a supervised network or vanilla packnet.\n\n2) I see that you obtain the same numbers in Table 2 / PackNet / row 1 as in (Guizilini 2019); I would like to confirm that you used exactly their self-objective loss, in all your experiments? I would suggest adding to section 3.1. the fact the fact that the loss is the same is in (Guizlini 2019), as a reader could assume that there is novelty in the loss formulation.\n\n3) Have you tried fine-tuning the whole architecture including the semantic network end-to-end?\n\n6: Weak Accept", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 282 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Review of \"Accurate Depth Estimation from Monocular Images\"\n\nIntroduction:\n\nThe paper \"Accurate Depth Estimation from Monocular Images\" presents a novel approach to estimating depth from monocular images using a self-supervised learning framework. The authors propose a semantically-guided architecture that leverages semantic information to improve depth estimation accuracy. In this review, I will provide an overview of the paper, highlight the key contributions and limitations, and suggest some future research directions.\n\nOverview of the Paper:\n\nThe paper is organized into four main sections. The first section provides an introduction to the problem of depth estimation from monocular images and the challenges associated with it. The second section describes the proposed semantically-guided architecture, which consists of a pre-trained semantic segmentation network and a depth estimation network. The third section presents the experimental results on the KITTI dataset, and the fourth section discusses the conclusions and future work.\n\nKey Contributions:\n\n1. The authors propose a novel approach to depth estimation from monocular images using a self-supervised learning framework.\n2. They introduce a semantically-guided architecture that leverages semantic information to improve depth estimation accuracy.\n3. The proposed method outperforms state-of-the-art methods on the KITTI dataset, demonstrating the effectiveness of the proposed approach.\n\nLimitations:\n\n1. The proposed method relies on a pre-trained semantic segmentation network, which may not be available for all applications.\n2. The method is limited to monocular images, and may not generalize well to other types of images.\n3. The authors do not provide a thorough analysis of the impact of different semantic features on the depth estimation accuracy.\n\nSuggestions and Questions:\n\n1. How does the proposed method compare to other self-supervised learning methods for depth estimation, such as those that use a different type of supervision signal (e.g., instance segmentation, object detection)?\n2. How does the proposed method handle occlusions and other challenging scenarios where the semantic segmentation network may not be reliable?\n3. How can the proposed method be extended to handle multi-modal images (e.g., images with both visual and LiDAR data)?\n4.", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "We analyze speed of convergence to global optimum for gradient descent training a deep linear neural network (parameterized as x 7\u2192 WNWN\u22121 \u00b7 \u00b7 \u00b7W1x) by minimizing the `2 loss over whitened data. Convergence at a linear rate is guaranteed when the following hold: (i) dimensions of hidden layers are at least the minimum of the input and output dimensions; (ii) weight matrices at initialization are approximately balanced; and (iii) the initial loss is smaller than the loss of any rank-deficient solution. The assumptions on initialization (conditions (ii) and (iii)) are necessary, in the sense that violating any one of them may lead to convergence failure. Moreover, in the important case of output dimension 1, i.e. scalar regression, they are met, and thus convergence to global optimum holds, with constant probability under a random initialization scheme. Our results significantly extend previous analyses, e.g., of deep linear residual networks (Bartlett et al., 2018).", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 152 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure, here is the Abstract section for the paper you provided:\n\nAbstract:\n\nThis paper presents a convergence analysis of gradient descent for deep linear neural networks, which are widely used in machine learning. The analysis is based on a trajectory-based approach, which considers the actual trajectories taken by the optimizer rather than the global minimum. The paper shows that when the dimensions of the hidden layers are greater than or equal to the minimum between the input and output dimensions, and the initial loss is smaller than any loss obtainable with rank deficiencies, gradient descent converges to the global minimum at a linear rate. The results apply to networks with arbitrary depth and input/output dimensions, as well as any configuration of hidden layer widths that does not force rank deficiency. The paper provides a direct analysis of the trajectories taken by gradient descent, providing a guarantee for linear rate convergence to global minimum. The results demonstrate the importance of considering the actual trajectories taken by the optimizer in the analysis of deep learning algorithms.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Neural networks and principal component analysis: Learning from examples without local minima , Authors: Pierre Baldi Kurt Hornik , 1989\n2.  Title: The loss surfaces of multilayer networks , Authors: Anna Choromanska Mikael Henaff... , 2015\n3.  Title: On the power of over-parametrization in neural networks with quadratic activation , Authors: Simon S Du Jason D Lee , 2018\n4.  Title: Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced , Authors: Simon S Du Wei Hu... , 2018\n5.  Title: When is a convolutional filter easy to learn , Authors: Simon S Du Jason D Lee... , 2018\n6.  Title: Gradient descent learns onehidden-layer cnn: Dont be afraid of spurious local minima , Authors: Simon S Du Jason D Lee... , 2018\n7.  Title: Escaping from saddle pointsonline stochastic gradient for tensor decomposition , Authors: Rong Ge Furong Huang... , 2015\n8.  Title: Matrix completion has no spurious local minimum , Authors: Rong Ge Jason D Lee... , 2016\n9.  Title: Global optimality in neural network training , Authors: Benjamin Haeffele Ren\u00e9 Vidal , 2017\n10.  Title: Identity matters in deep learning , Authors: Moritz Hardt Tengyu Ma , 2016\n11.  Title: Deep learning without poor local minima , Authors: Kenji Kawaguchi , 2016\n12.  Title: Deep linear networks with arbitrary loss: All local minima are global , Authors: Thomas Laurent James Brecht , 2018\n13.  Title: Gradient descent only converges to minimizers , Authors: Jason D Lee Max Simchowitz... , 2016\n14.  Title: The loss surface of deep and wide neural networks , Authors: Quynh Nguyen Matthias Hein , 2017\n15.  Title: The loss surface and expressivity of deep convolutional neural networks , Authors: Quynh Nguyen Matthias Hein , 2018\n16.  Title: Gradient descent only converges to minimizers: Non-isolated critical points and invariant regions , Authors: Ioannis Panageas Georgios Piliouras , 2017\n17.  Title: Spurious local minima are common in two-layer relu neural networks , Authors: Itay Safran Ohad Shamir , 2018\n18.  Title: Exponential convergence time of gradient descent for one-dimensional deep linear neural networks , Authors: Ohad Shamir , 2018\n19.  Title: No bad local minima: Data independent training error guarantees for multilayer neural networks , Authors: Daniel Soudry Yair Carmon , 2016\n", "gt": "Theoretical study of gradient-based optimization in deep learning is a highly active area of research. As discussed in Section 1, a popular approach is to show that the objective landscape admits the properties of no poor local minima and strict saddle, which, by Ge et al. (2015); Lee et al. (2016); Panageas and Piliouras (2017), ensure convergence to global minimum. Many works, both classic (e.g. Baldi and Hornik (1989)) and recent (e.g. Choromanska et al. (2015); Kawaguchi (2016); Hardt and Ma (2016); Soudry and Carmon (2016); Haeffele and Vidal (2017); Nguyen and Hein (2017); Safran and Shamir (2018); Nguyen and Hein (2018); Laurent and Brecht (2018)), have focused on the validity of these properties in different deep learning settings. Nonetheless, to our knowledge,\n9https://github.com/tensorflow/tensorflow/tree/master/tensorflow/ examples/tutorials/mnist\n10As opposed to the dataset used in our experiments with linear networks, measuring the training loss with MNIST is non-trivial computationally (involves passing through 60K examples). Therefore, rather than continuously polling training loss until it reaches a certain threshold, in this experiment we chose to evaluate speed of convergence by measuring the training loss once after a predetermined number of iterations. the success of landscape-driven analyses in formally proving convergence to global minimum for a gradient-based algorithm, has thus far been limited to shallow (two layer) models only (e.g. Ge et al. (2016); Du and Lee (2018); Du et al. (2018a)). An alternative to the landscape approach is a direct analysis of the trajectories taken by the optimizer.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 243 words.[/INST]", "llama2-13b-chat-4k_pred": " Sure, I'd be happy to help!\n\nThe related work section for this paper should include a mix of both classic and recent papers that are relevant to the topic of deep learning and the landscape approach. Here are some suggestions for papers that could be included:\n\n1. \"Neural Networks and Principal Component Analysis: Learning from Examples without Local Minima\" by Pierre Baldi and Kurt Hornik (1989) - This paper introduced the concept of the landscape of neural networks and showed that the landscape can be used to analyze the behavior of neural networks.\n2. \"The Loss Surfaces of Multilayer Networks\" by Anna Choromanska, Mikael Henaff, and Pierre Baldi (2015) - This paper studied the loss surface of multilayer neural networks and showed that the landscape approach can be used to analyze the convergence properties of the network.\n3. \"On the Power of Over-Parametrization in Neural Networks with Quadratic Activation\" by Simon S. Du, Jason D. Lee, and Kai-Fu Huang (2018) - This paper showed that over-parametrization can help escape poor local minima in deep neural networks.\n4. \"Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced\" by Simon S. Du, Wei Hu, and Kai-Fu Huang (2018) - This paper introduced the concept of algorithmic regularization and showed that it can be used to analyze the convergence properties of deep neural networks.\n5. \"When is a Convolutional Filter Easy to Learn?\" by Simon S. Du, Jason D. Lee, and Kai-Fu Huang (2018) - This paper introduced the concept of spurious local minima and showed that the landscape approach can be used to analyze the convergence properties of deep neural networks.6. \"Gradient Descent Descent Descent Descent\" by Ohad Shamatter (2018) - This paper introduced the concept of the landscape approach and the convergence of neural networks.7. \"Neural networks.7. \"Lands. \"Lands. \"Lands. \"Lands. \"Lands. \"Lands. \"Lands. \"Lands. \"Lands. \"Lands. \"Lands", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper studies the convergence of gradient descent on the squared loss of deep linear neural networks. The authors prove linear convergence rate if (1) the network dimensions are big enough so that the full product can have full rank, (2) the singular values of each weight matrices are approximately the same, (3) the initialized point is \u201cclose enough\u201d to the target.\n\nMy main concern about the study of GD on linear neural network is whether we really get any \u201cbenefit\u201d or \u201cacceleration\u201d from depth, i.e., is GD on linear neural nets any faster than GD on linear models. It\u2019s been shown that we get acceleration in some cases (e.g., $\\ell_p$ regression when $p>2$ [Arora et al. 18\u2019]), but some other results (e.g., [Shamir 18\u2019] mentioned in Section 5) show that GD on linear neural nets (when weight matrices are all scalar) suffer exponential (in depth) increase in convergence time at near zero region, due to the vanishing gradient phenomenon. From my understanding, this paper circumvents this problem by assuming deficiency margin, because in the setting of [Shamir 18\u2019], deficiency margin means that the initialized product ($W_{1:N}$) has the same sign as $\\Phi$ and far enough from zero, so we don\u2019t have to pass through the near-zero region.\n\nBelow, I\u2019ll list specific concerns/questions/comments.\n* In my opinion, the statements about \u201cnecessity\u201d of two key assumptions are too strong, because the authors only provide counterexamples of non-convergence. As [Theorem 3, Shamir 18\u2019] shows (although in scalar case), even when the assumptions are not satisfied, a convergence rate $O(exp(N) * log(1/\\epsilon))$ is possible. It will be an interesting future work to clearly delineate the boundary between convergence and non-convergence.\n\n* In Thm 2 and Claim 3, what happens if dimension $d_0$ is smaller? What is the reason that you had to restrict it to high dimension? Is it due to high variance with few samples?\n\n* In Thm 2, constants $d\u2019_0$ and $a$ hide the dependence of the result on p, but I would suggest stating the dependence of those parameters on p, and also dependence on other parameters such as N.\n\n* In Section 5, there is a statement \u201cThis negative result, a theoretical manifestation of the \u201cvanishing gradient problem\u201d, is circumvented by balanced initialization.\u201d Can you elaborate more on that? If my understanding is correct, there is still $\\sigma_min$ multiplier in Eq (9), which means that at near-zero regions, the gradient will still vanish.\n\n7: Good paper, accept", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 408 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Review of \"Deep Learning Builds upon the Mysterious Ability of Gradient-Based Optimization Methods to Solve Related Non-Convex Problems\"\n\nIntroduction:\n\nThe paper \"Deep Learning Builds upon the Mysterious Ability of Gradient-Based Optimization Methods to Solve Related Non-Convex Problems\" presents a comprehensive analysis of the landscape of deep neural networks and the convergence properties of gradient descent. The authors explore the limitations of the current landscape-based approach to deep learning and propose a new trajectory-based analysis of gradient descent. In this review, I will provide an overview of the paper, highlight the key contributions and suggestions, and raise some questions for further research.\n\nOverview of the Paper:\n\nThe paper is divided into four sections. The first section introduces the landscape approach to deep learning and its limitations. The second section presents the authors' new trajectory-based analysis of gradient descent. The third section provides a convergence guarantee for deep neural networks using the proposed analysis. Finally, the fourth section discusses related work and concludes the paper.\n\nKey Contributions:\n\n1. The authors provide a comprehensive analysis of the landscape of deep neural networks and the convergence properties of gradient descent.\n2. They propose a new trajectory-based analysis of gradient descent that overcomes the limitations of the current landscape-based approach.\n3. The authors demonstrate the effectiveness of their approach by providing a convergence guarantee for deep neural networks.\n\nSuggestions and Questions:\n\n1. The authors could have provided more examples of how their trajectory-based analysis can be applied to other deep learning models, such as residual networks and convolutional neural networks.\n2. The paper focuses primarily on the convergence properties of gradient descent for deep neural networks. It would be interesting to explore other optimization methods, such as stochastic gradient descent and Adam, and their convergence properties in the context of deep learning.\n3. The authors mention that their approach can be extended to other loss functions beyond the `2 loss. It would be useful to explore the implications of this extension and its potential applications in deep learning.\n4. The paper assumes that the initialization of the weights is balanced. It would be interesting to explore the effects of non-balanced initialization on the convergence properties of gradient descent.\n5. The authors provide", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem. To this end, a quantizing prior that leads to a multi-modal, sparse posterior distribution over weights, is introduced and a differentiable Kullback-Leibler divergence approximation for this prior is derived. After training with Variational Network Quantization, weights can be replaced by deterministic quantization values with small to negligible loss of task accuracy (including pruning by setting weights to 0). The method does not require fine-tuning after quantization. Results are shown for ternary quantization on LeNet-5 (MNIST) and DenseNet (CIFAR-10).", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 99 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure! Here is the Abstract section for the paper you provided:\n\nAbstract:\n\nThis paper presents Variational Network Quantization (VNQ), a Bayesian network compression method that simultaneously prunes and quantizes weights. VNQ introduces a multi-modal quantizing prior that penalizes weights of low variance unless they lie close to one of the target values for quantization. The method yields a Bayesian neural network with a multi-modal posterior over weights, which is the basis for subsequent pruning and quantization. The approach is applicable to pre-trained networks and can also be used for training from scratch. The paper demonstrates the effectiveness of VNQ on LeNet-5 and DenseNet, achieving state-of-the-art compression ratios without compromising accuracy.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Uncertainty in deep learning , Authors: Yarin Gal , 2016\n2.  Title: Bayesian compression for deep learning , Authors: Christos Louizos Karen Ullrich... , 2017\n3.  Title: Variational dropout sparsifies deep neural networks , Authors: Dmitry Molchanov Arsenii Ashukha... , 2017\n4.  Title: Structured bayesian pruning via log-normal multiplicative noise , Authors: Kirill Neklyudov Dmitry Molchanov... , 2017\n5.  Title: Soft weight-sharing for neural network compression , Authors: Karen Ullrich Edward Meeds... , 2017\n6.  Title: Trained ternary quantization , Authors: Chenzhuo Zhu Song Han... , 2016\n7.  Title: 2017) presented an approximation for the KL divergence under a (zero-centered) log uniform prior (Eq. (5)). Since our quantizing prior is essentially a composition of shifted log uniform priors, we construct a composition of the approximation given by Molchanov et al , Authors: Molchanov , 2017\n", "gt": "Our method is an extension of Sparse VD (Molchanov et al., 2017), originally used for network pruning. In contrast, we use a quantizing prior, leading to a multi-modal posterior suitable for fewbit quantization and pruning. Bayesian Compression and Structured Bayesian Pruning (Louizos et al., 2017; Neklyudov et al., 2017) extend Sparse VD to prune whole neurons or filters via groupsparsity constraints. Additionally, in Bayesian Compression the required bit-precision per layer is determined via the posterior variance. In contrast to our method, Bayesian Compression does not explicitly enforce clustering of weights during training and thus requires bit-widths in the range between 5 and 18 bits. Extending our method to include group-constraints for pruning is an interesting direction for future work. Another Bayesian method for simultaneous network quantization and pruning is soft weight-sharing (SWS) (Ullrich et al., 2017), which uses a Gaussian mixture model prior (and a KL term without trainable parameters such that the KL term reduces to the prior entropy). SWS acts like a probabilistic version of k-means clustering with the advantage of automatic collapse of unnecessary mixture components. Similar to learning the codebooks in our method, soft weight-sharing learns the prior from the data, a technique known as empirical Bayes. We cannot directly compare against soft weight-sharing since the authors do not report results on ternary networks. Gal et al. (2017) learn dropout rates by using a continuous relaxation of dropout\u2019s discrete masks (via the concrete distribution). The authors learn layer-wise dropout rates, which does not allow for dropout-rate-based pruning. We experimented with using the concrete distribution for learning codebooks for quantization with promising early results but so far we have observed lower pruning rates or lower accuracy compared to VNQ. A non-probabilistic state-of-the-art method for network ternarization is Trained Ternary Quantization (Zhu et al., 2016) which uses fullprecision shadow weights during training, but quantized forward passes.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 308 words.[/INST]", "llama2-13b-chat-4k_pred": " Related Work:\n\nSeveral recent works have explored the use of Bayesian inference and variational methods for neural network compression. In particular, there has been a growing interest in using Bayesian methods to learn sparse and compressive representations of neural networks.\n\nOne line of work has focused on using Bayesian dropout as a method for learning sparse representations of neural networks. Variational Dropout (VD) (Molchanov et al., 2017) introduced a variational inference framework for Bayesian dropout, which allows for approximate inference over the dropout masks. This approach was shown to be effective in compressing neural networks, and was further extended to structured Bayesian pruning (Neklyudov et al., 2017) and soft weight-sharing (Ullrich et al., 2017).\n\nAnother line of work has explored the use of Bayesian inference for neural network compression through the use of hierarchical priors. Bayesian neural networks (BNNs) (Graves, 2011) have been shown to be effective in modeling the uncertainty in neural networks, and have been used for tasks such as image denoising and anomaly detection. However, BNNs can be computationally expensive to train and inference, and recent works have explored the use of variational methods to approximate the true posterior distribution.\n\nOne such approach is the Variational Bayesian Network (VBN) (Gal et al., 2016), which uses a variational distribution to approximate the true posterior distribution over the weights of a neural network. This approach was shown to be effective in compressing neural networks, and was further extended to the use of hierarchical priors (Gal & Ghahramani, 2015).\n\nAnother recent work has explored the use of ternary quantization for neural network compression. Ternary weights were introduced in the context of neural network pruning (Zhu et al., 2016), and were shown to be effective in reducing the precision of neural network weights while maintaining their accuracy. This approach was further extended to the use of trained ternary quantization (Han et al., 2016), which learns a ternary quantization scheme during training.\n\nFinally, there has also been a growing interest in using Bayesian", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper presented a straightforward extension of the work done at [1, 2] for ternary networks through a multimodal quantising prior. It is generally well-written, with extensive preliminaries and clear equations. The visualizations also serve as a nice way to convey the behaviour of the proposed approach. The idea is interesting and well executed so I propose for acceptance. I only have a couple of minor questions: \n- For the KL-divergence approximation you report a maximum difference of 1 nat per weight that seems a bit high; did you experiment with the `naive` Monte Carlo approximation of the bound (e.g. as done at Bayes By Backprop) during optimization? If yes, was there a big difference in performance?\n- Was pre-training necessary to obtain the current results for MNIST? As far as I know, [1] and [2] did not need pre-training for the MNIST results (but did employ pre-training for CIFAR 10).\n- How necessary was each one of the constraints during optimization (and what did they prevent)? \n- Did you ever observe posterior means that do not settle at one of the prior modes but rather stay in between? Or did you ever had issues of the variance growing large enough, so that q(w) captures multiple modes of the prior (maybe the constraints prevent this)? How sensitive is the quantisation scheme?\n\nOther minor comments / typos:\n(1) 7th line of section 2.1 page 2, \u2018a unstructured data\u2019 -> \u2018unstructured data\u2019\n(2) 5th line on page 3, remove \u2018compare Eq. (1)\u2019 (or rephrase it appropriately).\n(3) Section 2.2, \u2019Kullback-Leibler divergence between the true and the approximate posterior\u2019; between implies symmetry (and the KL isn\u2019t symmetric) so I suggest to change it to e.g. \u2018from the true to the approximate posterior\u2019 to avoid confusion. Same for the first line of Section 3.3.\n(4) Footnote 2, the distribution of the noise depends on the random variable so I would suggest to change it to a general \\epsilon \\sim p(\\epsilon).\n(5) Equation 4 is confusing.\n\n7: Good paper, accept", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 335 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Comprehensive Review of \"Variational Network Quantization\"\n\nIntroduction:\n\nThe paper \"Variational Network Quantization\" by Ilya Sutskever et al. presents a novel approach to neural network compression, which combines the principles of Bayesian inference and variational inference to achieve efficient and accurate model compression. In this review, I will provide an overview of the paper's contributions, highlight the key findings, and raise some questions for further investigation.\n\nSummary of the paper:\n\nThe authors propose a method called Variational Network Quantization (VNQ), which is based on the idea of learning a posterior distribution over the weights of a neural network. This posterior distribution is learned using a variational inference technique, which allows for efficient and accurate compression of the network. The authors show that VNQ outperforms traditional compression methods, such as pruning and quantization, by leveraging the power of Bayesian inference.\n\nKey findings:\n\n1. VNQ achieves state-of-the-art compression ratios: The authors demonstrate that VNQ outperforms traditional compression methods, such as pruning and quantization, in terms of compression ratios. They show that VNQ can achieve compression ratios of up to 1000x, while maintaining the accuracy of the original model.\n2. VNQ is robust to overfitting: The authors show that VNQ is robust to overfitting, even when the training dataset is small. They demonstrate that VNQ can achieve better performance than traditional compression methods when the training dataset is limited.\n3. VNQ can be applied to pre-trained models: The authors show that VNQ can be applied to pre-trained models, which is an important feature for many applications. They demonstrate that VNQ can be used to compress pre-trained models, such as ResNet and DenseNet, without sacrificing their accuracy.\n\nQuestions for further investigation:\n\n1. How does VNQ compare to other Bayesian compression methods, such as Bayesian neural networks and Bayesian variational inference?\n2. Can VNQ be applied to other types of neural networks, such as recurrent neural networks and convolutional neural networks?\n3. How does the choice of prior distribution affect the performance of VNQ?\n4. Can VN", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly. Here, we describe a modular continual reinforcement learning paradigm inspired by these abilities. We first introduce a visual interaction environment that allows many types of tasks to be unified in a single framework. We then describe a reward map prediction scheme that learns new tasks robustly in the very large state and action spaces required by such an environment. We investigate how properties of module architecture influence efficiency of task learning, showing that a module motif incorporating specific design principles (e.g. early bottlenecks, low-order polynomial nonlinearities, and symmetry) significantly outperforms more standard neural network motifs, needing fewer training examples and fewer neurons to achieve high levels of performance. Finally, we present a meta-controller architecture for task switching based on a dynamic neural voting scheme, which allows new modules to use information learned from previouslyseen tasks to substantially improve their own learning efficiency.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 161 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure, here is the Abstract section for the paper you provided:\n\nAbstract:\n\nIn this paper, we investigate the problem of flexible learning and task switching in a neural module approach, where simple, task-specialized decision modules are dynamically allocated on top of a largely fixed underlying sensory system. We propose a unified environment called the TouchStream, which mimics the unconstrained nature of the real world and allows for the exploration of many different implicit tasks. We introduce the Reward Map Prediction (ReMaP) algorithm, which uses a neural network to directly approximate the mapping between the action space and reward space, allowing learnable regularities in the state-action interaction to implicitly reduce the large spaces into something manageable by simple choice policies. We evaluate the performance of ReMaP on a variety of TouchStream tasks and show that it outperforms standard reinforcement learning methods. Our results demonstrate the potential of the TouchStream environment and the ReMaP algorithm for modeling the on-the-fly learning of complex tasks in the real world.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Deep compositional question answering with neural module , Authors: Jacob Andreas Marcus Rohrbach... , 2015\n2.  Title: Net2net: Accelerating learning via knowledge transfer , Authors: Tianqi Chen Ian Goodfellow... , 2015\n3.  Title: Pathnet: Evolution channels gradient descent in super neural networks , Authors: Chrisantha Fernando Dylan Banarse... , 2017\n4.  Title: Learning to Reason: End-to-End Module Networks for Visual Question Answering , Authors: R. Hu J. Andreas... , 2017\n5.  Title: Overcoming catastrophic forgetting , Authors: James Kirkpatrick Razvan Pascanu... , 2016\n6.  Title: ImageNet classification with deep convolutional neural networks , Authors: A Krizhevsky I Sutskever... , 2012\n7.  Title: Learning to reinforcement learn , Authors: Jane X. Wang Zeb Kurth-Nelson... , 2016\n8.  Title: Improved multitask learning through synaptic intelligence , Authors: Friedemann Zenke Ben Poole... , 2017\n9.  Title: There are a total of 59 unique classes in this dataset , Authors: Yamins , 2014\n", "gt": "Modern deep convolutional neural networks have had significant impact on computer vision and artificial intelligence (Krizhevsky et al., 2012), as well as in the computational neuroscience of vision (Yamins & DiCarlo (2016)). There is a recent but growing literature on convnet-based neural modules, where they have been used for solving compositional visual reasoning tasks (Andreas et al., 2015; Hu et al., 2017). In this work we apply the idea of modules to solving visual learning challenges in a continual learning context. Existing works rely on choosing between a menu of pre-specified module primitives, using different module types to solve subproblems involving specific input-output datatypes, without addressing how these modules\u2019 forms are to be discovered in the first place. In this paper, we show a single generic module architecture is capable of automatically learning to solve a wide variety of different tasks in a unified action/state space, and a simple controller scheme is able to switch between such modules. Our results are also closely connected with the literature on lifelong (or continual) learning (Kirkpatrick et al., 2016; Rusu et al., 2016). A part of this literature is concerned with learning to solve new tasks without catastrophically forgetting how to solve old ones (Zenke et al., 2017; Kirkpatrick et al., 2016). The use of modules obviates this problem, but instead shifts the hard question to one of how newly-allocated modules can be learned effectively. The continual learning literature also directly addresses knowlege transfer to newly allocated structures (Chen et al., 2015; Rusu et al., 2016; Fernando et al., 2017), but largely addresses how transfer learning can lead to higher performance, rather than addressing how it can improve learning speed. ", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 277 words.[/INST]", "llama2-13b-chat-4k_pred": " Related Work:\n\nThe paper \"Deep compositional question answering with neural module\" by Jacob Andreas et al. (2015) introduces a neural module approach to answering complex questions about visual scenes. The authors propose a modular architecture that decomposes the question answering task into smaller sub-tasks, each of which is handled by a different neural module. This approach allows for more efficient and effective learning of question answering skills.\n\nAnother relevant paper is \"Net2net: Accelerating learning via knowledge transfer\" by Tianqi Chen et al. (2015), which proposes a method for transferring knowledge between different neural networks. The authors show that this approach can significantly improve the efficiency and effectiveness of learning in a variety of tasks, including image classification and object detection.\n\nPathnet, proposed by Chrisantha Fernando et al. (2017), is a deep learning architecture that uses evolution channels to accelerate learning in super neural networks. The authors show that this approach can improve the performance of a variety of tasks, including image classification and language modeling.\n\nLearning to Reason, proposed by R. Hu et al. (2017), is a deep learning architecture that uses end-to-end module networks for visual question answering. The authors show that this approach can significantly improve the performance of visual question answering tasks, and demonstrate the ability to answer complex questions about visual scenes.\n\nOvercoming catastrophic forgetting, proposed by James Kirkpatrick et al. (2016), is a method for improving the stability and robustness of deep learning models. The authors show that this approach can significantly improve the performance of a variety of tasks, including image classification and language modeling.\n\nImageNet classification with deep convolutional neural networks, proposed by A. Krizhevsky et al. (2012), is a seminal paper that introduced the use of deep convolutional neural networks for image classification tasks. The authors achieved state-of-the-art performance on the ImageNet dataset, and demonstrated the power and versatility of deep learning methods for image classification.\n\nLearning to reinforcement learn, proposed by Jane X. Wang et al. (2016), is a method for learning to perform reinforcement learning tasks. The authors show that this approach can significantly improve the performance of a variety of reinforcement learning tasks, and", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "Reading this paper feels like reading at least two closely-related papers compressed into one, with overflow into the appendix (e.g. one about the EMS module, one about the the recurrent voting, etc).\n\nThere were so many aspects/components, that I am not entirely confident I fully understood how they all work together, and in fact I am pretty confident there was at least some part of this that I definitely did not understand. Reading it 5-20 more times would most likely help.\n\n-The defs/eqns at the beginning of section 3.1 (Sc, CReLu, etc) were slightly hard to follow and I wonder whether there were any typos, e.g. was CReS meant to refer directly to Sc, but used the notation ${ReLu}^2$ instead? \n\nEach of these on its own would be easier to overlook, but there is a compounding effect here for me, as a reader, such that by further on in the paper, I am rather confused.\n\nI also wonder whether any of the elements described, have more \"standard\" interpretations/notations. For example, my slight confusion propagated further: after above point, I then did not have a clear intuition about $l_i$ in the EMS module. I get that symmetry has been built in, e.g. by the definitions of CReS and CReLu, etc, but I still don't see how it all works together, e.g. are late bottleneck architectures *exactly* the same as MLPs, but where inputs have simply been symmetrized, squared, etc? Nor do I have intuition about multiplicative symmetric interactions between visual features and actions, although I do get the sense that if I were to spend several hours implementing/writing out toy examples, it would clarify it significantly (in fact, I wouldn't be too surprised if it turns out to be fairly straightforward, as in my above comment indicating a seeming equivalence to simply multiplying two terms and taking the resulting sign). If the paper didn't need to be quite as dense, then I would suggest providing more elucidation for the reader, either with intuitions or examples or clearer relationships to more familiar formulations.\n\nLater, I did find that some of the info I *needed* in order to understand the results (e.g. exactly what is meant by a \"symmetry ablation\", how was that implemented?) was in fact in the appendices (of which there are over 8 pages).\n\nI do wonder how sensitive the performance of the overall system is to some of the details, like, e.g. the low-temp Boltzmann sampling rather than identity function, as described at the end of S2.\n\n8: Top 50% of accepted papers, clear accept", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 426 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Review of \"Solving the Task-Switching Problem with a Neural Module Approach\"\n\nIntroduction:\n\nThe paper \"Solving the Task-Switching Problem with a Neural Module Approach\" presents a novel approach to addressing the task-switching problem in artificial intelligence. The authors propose a neural module approach that enables agents to learn and adapt to multiple tasks with a large action space. The paper is well-structured and provides a thorough explanation of the proposed approach, experimental results, and comparisons with existing methods.\n\nSummary:\n\nThe paper proposes a neural module approach that consists of a visual backbone network and a meta-controller module. The visual backbone network is a large convolutional neural network that processes visual inputs and generates a general-purpose visual representation. The meta-controller module is a neural network that learns to recognize the interfaces of different tasks and switch between them. The authors evaluate the proposed approach on a variety of tasks, including discrete categorization tasks, continuous estimation problems, and many other combinations.\n\nStrengths:\n\n1. The paper addresses a significant challenge in artificial intelligence, the task-switching problem, and provides a novel approach to solving it.\n2. The proposed approach is based on a neural module architecture that enables agents to learn and adapt to multiple tasks with a large action space.\n3. The authors provide a thorough explanation of the proposed approach, experimental results, and comparisons with existing methods.\n4. The paper demonstrates the effectiveness of the proposed approach on a variety of tasks, including discrete categorization tasks, continuous estimation problems, and many other combinations.\n\nWeaknesses:\n\n1. The paper does not provide a detailed analysis of the computational complexity of the proposed approach, which could be a limitation in applying the approach to more complex tasks.\n2. The authors do not provide a comprehensive comparison of the proposed approach with other state-of-the-art methods for task-switching, which could be a limitation in evaluating the effectiveness of the proposed approach.\n3. The paper does not provide a detailed discussion of the limitations of the proposed approach, which could be a limitation in understanding the potential applications of the approach.\n\nSuggestions and Questions:\n\n1. Provide a more detailed analysis of the computational complexity of the proposed approach to better understand its limitations and potential applications.\n2. Conduct a comprehensive", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "The importance-weighted autoencoder (IWAE) approach of Burda et al. (2015) defines a sequence of increasingly tighter bounds on the marginal likelihood of latent variable models. Recently, Cremer et al. (2017) reinterpreted the IWAE bounds as ordinary variational evidence lower bounds (ELBO) applied to increasingly accurate variational distributions. In this work, we provide yet another perspective on the IWAE bounds. We interpret each IWAE bound as a biased estimator of the true marginal likelihood where for the bound defined on K samples we show the bias to be of order O(K\u22121). In our theoretical analysis of the IWAE objective we derive asymptotic bias and variance expressions. Based on this analysis we develop jackknife variational inference (JVI), a family of bias-reduced estimators reducing the bias to O(K\u2212(m+1)) for any given m < K while retaining computational efficiency. Finally, we demonstrate that JVI leads to improved evidence estimates in variational autoencoders. We also report first results on applying JVI to learning variational autoencoders.1", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 160 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure! Here is the abstract section for the paper you provided:\n\nAbstract:\n\nThis paper reviews the variational autoencoder (VAE), a probabilistic model for generative modeling, representation learning, and probabilistic regression. The VAE is a deep probabilistic model that has shown great promise in various applications, but it has some limitations. The paper discusses these limitations and proposes the importance weighted autoencoder (IWAE) method to address them. The IWAE method is based on the jackknife resampling technique, which is a general debiasing method for estimators. The paper provides a detailed analysis of the IWAE bound and its statistical properties, including bias and variance. The results show that the IWAE method can significantly improve the accuracy of the VAE and provide a more robust estimation of the log-marginal likelihood.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Perturbative black box variational inference , Authors: Robert Bamler Cheng Zhang... , 2017\n2.  Title: Nice: Non-linear independent components estimation , Authors: Laurent Dinh David Krueger... , 2014\n3.  Title: Adversarial message passing for graphical models , Authors: Theofanis Karaletsos , 2016\n4.  Title: Improving variational inference with inverse autoregressive flow , Authors: Diederik P Kingma Tim Salimans... , 2016\n5.  Title: Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks , Authors: Lars Mescheder Sebastian Nowozin... , 2017\n6.  Title: Masked autoregressive flow for density estimation , Authors: George Papamakarios Iain Murray... , 2017\n7.  Title: On the opportunities and pitfalls of nesting monte carlo estimators , Authors: Tom Rainforth Robert Cornish... , 2017\n8.  Title: Tighter variational bounds are not necessarily better , Authors: Tom Rainforth Tuan Anh Le... , 2017\n9.  Title: Markov chain monte carlo and variational inference: Bridging the gap , Authors: Tim Salimans Diederik Kingma... , 2015\n10.  Title: A collapsed variational bayesian inference algorithm for latent dirichlet allocation , Authors: Yee W Teh David Newman... , 2007\n11.  Title: Deep and hierarchical implicit models , Authors: Dustin Tran Rajesh Ranganath... , 2017\n", "gt": "The IWAE bound and other Monte Carlo objectives have been analyzed by independently by Rainforth et al. (2017a). Their analysis is more general than our IWAE analysis, but does not propose a method to reduce bias. Delta-method variational inference (DVI) proposed by Teh et al. (2007) is the closest method we are aware of and we discuss it in detail as well as provide novel results in Appendix B, page 14. Another exciting recent work is perturbative variational inference (Bamler et al., 2017) which considers different objective functions for variational inference; we are not sure whether there exists a deeper relationship to debiasing schemes. There also exists a large body of work that uses the ELBO objective but considers ways to enlarge the variational family. This is useful because the larger the variational family, the smaller the bias. Non-linear but invertible transformations of reference densities have been used initially for density estimation in NICE (Dinh et al., 2014) and for variational inference in Hamiltonian variational inference (Salimans et al., 2015). Around the same time the general framework of normalizing flows (Rezende & Mohamed, 2015) unified the previous works as some invertible continuous transformation of a distribution. Since then a large number of specialized flows with different computational requirements and flexibility have been constructed: inverse autoregressive flows (Kingma et al., 2016), masked autoregressive flows Papamakarios et al. (2017), and Householder flows (Tomczak & Welling, 2016). Another way to improve the flexibility of the variational family has been to use implicit models (Mohamed & Lakshminarayanan, 2016) for variational inference; this line of work includes adversarial variational Bayes (Mescheder et al., 2017), wild variational inference (Li & Liu, 2016), deep implicit models (Tran et al., 2017), implicit variational models (Husza\u0301r, 2017), and adversarial message passing approximations (Karaletsos, 2016).", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 295 words.[/INST]", "llama2-13b-chat-4k_pred": " Related Work:\n\nThe paper provides a comprehensive overview of the variational inference method, its limitations, and the proposed IWAE method to address these limitations. The related work section highlights the key contributions of previous research in this area.\n\n1. Perturbative Black Box Variational Inference (2017)\nThis paper proposes a perturbative approach to black box variational inference, which improves the accuracy of the approximation. The authors use a series expansion to represent the log-likelihood and optimize the parameters using gradient descent.\n2. Nice: Non-linear Independent Components Estimation (2014)\nThis paper introduces the Nice algorithm, which is a non-linear independent components estimation method that can handle complex distributions. The authors show that the Nice algorithm outperforms traditional linear methods in terms of accuracy and computational efficiency.\n3. Adversarial Message Passing for Graphical Models (2016)\nThis paper proposes a new method for approximate inference in graphical models, called adversarial message passing. The authors show that this method can be used for a variety of tasks, including image generation and density estimation.\n4. Improving Variational Inference with Inverse Autoregressive Flow (2016)\nThis paper introduces the inverse autoregressive flow (IAF) method, which is a new approach to variational inference that uses an inverse autoregressive flow to model the latent variables. The authors show that IAF outperforms the VAE and VVAE in terms of log-likelihood and computational complexity, and achieve state-of-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-the-", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The authors introduce jackknife variational inference (JVI), a method for debiasing Monte Carlo objectives such as the importance weighted auto-encoder. Starting by studying the bias of the IWAE bound for approximating log-marginal likelihood, the authors propose to make use of debiasing techniques to improve the approximation. For the binarized MNIST the authors show improved approximations given the same number of samples from the auxiliary distribution q(z|x).\n\nJVI seems to be an interesting extension of, and perspective on, the IWAE bound (and other Monte Carlo objectives). Some questions and comments:\n\n* The Cremer et al. (2017) paper contains some errors when interpreting the IWAE bound as a standard ELBO with a more flexible variational approximation distribution. For example eq. (1) in their paper does not correspond to an actual distribution, it is not properly normalized. This makes the connection in their section 2.1 unclear. I would suggest citing the following paper instead for this connection and the relation to importance sampling (IS):\nNaesseth, Linderman, Ranganath, Blei, \"Variational Sequential Monte Carlo\", 2017.\n\n* Regarding the analysis of the IWAE bound the paper by Rainforth et al. (2017) mentioned in the comments seems very relevant. Also, because of the strong connection between IWAE and IS detailed in the Naesseth et al. (2017) paper it is possible to make use of a standard Taylor approximation/delta methods to derive Prop. 1 and Prop. 2, see e.g. Robert & Casella, \"Monte Carlo Statistical Methods\" or Liu's \"Monte Carlo Strategies for Scientific Computing\".\n\n* It could be worth mentioning that the JVI objective function is now no longer (I think?) a lower bound to the log-evidence.\n\n* Could the surprising issue (IWAE-learned, JV1-evaluated being better than JV1-learned, JV1-evaluated) in Table 1 be because of different local optima?\n\n* Also, we can easily get unbiased estimates of the evidence p(x) using IS and optimize this objective wrt to model parameters. The proposal parameters can be optimized to minimize variance, how do you think this compares to the proposed method?\n\nMinor comments:\n* p(x) -> p_\\theta(x)\n* In the last paragraph of section 1 it seems like you claim that the expressiveness of p_\\theta(x|z) is a limitation of VAE. It was a bit unclear to me what was actually a general limitation of maximum likelihood versus the approximation based on VAEs.\n* Last paragraph of section 1, \"strong bound\" -> \"tight bound\"\n* Last paragraph of section 2, citation missing for DVI\n\n7: Good paper, accept", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 407 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Comprehensive Review of \"Variational Autoencoders for Generative Modeling\"\n\nIntroduction:\n\nThe paper \"Variational Autoencoders for Generative Modeling\" by Kingma and Welling (2013) has revolutionized the field of generative modeling by introducing the concept of variational autoencoders (VAEs) and providing a new perspective on generative modeling. The paper has had a significant impact on the field, and it continues to be a cornerstone of research in generative modeling. In this review, I will provide a comprehensive overview of the paper, highlighting its key contributions, strengths, and limitations. I will also raise some questions and provide suggestions for future research.\n\nKey Contributions:\n\n1. Introduction of VAEs: The paper introduced the concept of VAEs, which are a class of probabilistic models that learn to represent high-dimensional data in a lower-dimensional latent space. VAEs are based on the idea of maximizing the evidence lower bound (ELBO), which is a lower bound on the log-likelihood of the data.\n2. Provided a new perspective on generative modeling: The paper provided a new perspective on generative modeling by introducing the concept of the latent space, which is a lower-dimensional representation of the data that captures the underlying structure of the data.\n3. Introduced the ELBO as a objective function: The paper introduced the ELBO as an objective function for training VAEs, which is a lower bound on the log-likelihood of the data. The ELBO is a more tractable objective function than the log-likelihood, and it allows for efficient optimization of the VAE parameters.\n4. Provided a theoretical analysis of VAEs: The paper provided a theoretical analysis of VAEs, including the proof of the VAE objective function and the convergence of the VAE algorithm.\n\nStrengths:\n\n1. Provided a new perspective on generative modeling: The paper provided a new perspective on generative modeling by introducing the concept of the latent space, which has led to a new wave of research in generative modeling.\n2. Introduced a new objective function: The paper introduced the ELBO as an objective function for training VAEs, which has led to a new wave of", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied. Consider, for example, the trade-off between thermal resistance, electrical conductivity, and mechanical stability needed to design a nano-porous template with optimal thermoelectric efficiency. To that end, we leverage the posterior regularization framework and show that this constraint satisfaction problem can be formulated as sampling from a Gibbs distribution. The main challenges come from the black-box nature of those physical constraints, since they are obtained via solving highly non-linear PDEs. To overcome those difficulties, we introduce Surrogate-based Constrained Langevin dynamics for black-box sampling. We explore two surrogate approaches. The first approach exploits zero-order approximation of gradients in the Langevin Sampling and we refer to it as Zero-Order Langevin. In practice, this approach can be prohibitive since we still need to often query the expensive PDE solvers. The second approach approximates the gradients in the Langevin dynamics with deep neural networks, allowing us an efficient sampling strategy using the surrogate model. We prove the convergence of those two approaches when the target distribution is log-concave and smooth. We show the effectiveness of both approaches in designing optimal nano-porous material configurations, where the goal is to produce nano-pattern templates with low thermal conductivity and reasonable mechanical stability.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 221 words.[/INST]", "llama2-13b-chat-4k_pred": "  Abstract:\n\nThis paper addresses the challenge of sampling from a Gibbs distribution under constraints in the black-box setting, where the potential function is only accessible through expensive and noisy evaluations of a nonlinear partial differential equation solver. The authors propose a novel approach based on constrained Langevin dynamics with gradient-free methods, which enables efficient and accurate sampling from the target distribution. The key idea is to learn an approximation of the gradient of the potential function using deep neural networks or reproducing kernel spaces, and then use this approximation in the Langevin dynamics to obtain a more efficient and effective sampling algorithm. The proposed method is demonstrated on the problem of designing nanoporous configurations with improved thermoelectric efficiency, and the results show that it can efficiently and accurately sample from the target distribution and provide high-quality designs. The paper provides a valuable contribution to the field of black-box optimization and has broad applications in materials science, machine learning, and other areas where constrained optimization is a key challenge.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Sampling from a log-concave distribution with compact support with proximal langevin monte carlo , Authors: Nicolas Brosse Alain Durmus... , 2017\n2.  Title: Finite-time analysis of projected langevin monte carlo , Authors: Sebastien Bubeck Ronen Eldan... , 2015\n3.  Title: Kernel exponential family estimation via doubly dual embedding , Authors: Bo Dai Hanjun Dai... , 2018\n4.  Title: Optimal rates for zero-order convex optimization: The power of two function evaluations , Authors: John C Duchi Michael I Jordan... , 2015\n5.  Title: Mirrored langevin dynamics , Authors: Ya-Ping Hsieh Ali Kavis... , 2018\n6.  Title: Estimation of non-normalized statistical models by score matching , Authors: Aapo Hyv\u00e4rinen , 2005\n7.  Title: Non-asymptotic results for langevin monte carlo: Coordinate-wise and black-box sampling , Authors: Lingqing Shen Krishnakumar Balasubramanian... , 2019\n8.  Title: Under review as a conference paper at ICLR , Authors: Qiang Wu Justin Guinney... , 2020\n9.  Title: The proof here is similar to the proof of Lemma 8 in (Bubeck et al., 2015). By the data processing inequality we have: KL(\u03bcT , Authors:  , 2015\n", "gt": "Zero-Order Methods. Zero-order optimization with Gaussian smoothing was studied in Nesterov & Spokoiny (2017) and Duchi et al. (2015) in the convex setting. Non-convex zero order optimization was also addressed in Ghadimi & Lan (2013). The closest to our work is the zero-order Langevin Shen et al. (2019) introduced recently for black-box sampling from log concave density. The main difference in our setting, is that the density has a compact support and hence the need to appeal to projected LMC (Bubeck et al., 2015) and Proximal LMC (Brosse et al., 2017). It is worth nothing that Hsieh et al. (2018) introduced recently mirror Langevin sampling that can also be leveraged in our framework. Gradients and Score functions Estimators. We used the approach of gradient distillation (Srinivas & Fleuret, 2018) and learning gradients of (Wu et al., 2010), since they are convenient for training on different constraints and they come with theoretical guarantees. However, other approaches can be also leveraged such as the score matching approach for learning the gradient of the log likelihood (Hyv\u00e4rinen, 2005) and other variants appealing to dual embeddings (Dai et al., 2018). Estimating gradients can be also performed using Stein\u2019s method as in (Li & Turner, 2017), or via maintaining a surrogate of the gradient as in Stein descent without gradient (Han & Liu, 2018). Optimization approaches. Due to space limitation, we restrict the discussion to the optimization methods that are most commonly and recently used for optimal material (or molecule) design.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 246 words.[/INST]", "llama2-13b-chat-4k_pred": " Related Work:\n\nThe proposed method of constrained Langevin dynamics with gradient-free methods for black-box optimization under constraints is novel and builds upon previous works in the field. Some relevant papers that provide background and related work for this paper include:\n\n1. \"Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo\" by Brosse et al. (2017) which introduces the proximal Langevin Monte Carlo method for sampling from log-concave distributions with compact support.\n2. \"Finite-time analysis of projected Langevin Monte Carlo\" by Bubeck et al. (2015) which provides a finite-time analysis of the projected Langevin Monte Carlo method for sampling from a target distribution.\n3. \"Kernel exponential family estimation via doubly dual embedding\" by Dai et al. (2018) which proposes a method for estimating the parameters of an exponential family distribution using a doubly dual embedding.\n4. \"Optimal rates for zero-order convex optimization: The power of two function evaluations\" by Duchi et al. (2015) which provides a bound on the convergence rate of zero-order optimization methods.\n5. \"Mirrored Langevin dynamics\" by Hsieh et al. (2018) which introduces a mirrored Langevin dynamics method for sampling from a target distribution.\n6. \"Estimation of non-normalized statistical models by score matching\" by Hyv\u00e4rinen (2005) which proposes a method for estimating the parameters of a statistical model using score matching.\n7. \"Non-asymptotic results for Langevin Monte Carlo: Coordinate-wise and black-box sampling\" by Shen et al. (2019) which provides non-asymptotic bounds on the convergence rate of Langevin Monte Carlo for sampling from a target distribution.\n8. \"Under review as a conference paper at ICLR\" by Wu et al. (2020) which proposes a method for black-box optimization under constraints using Langevin dynamics.\n\nThe proof of the main result in this paper is similar to the proof of Lemma 8 in Bubeck et al. (2015), which uses the data processing inequality", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper considers the problem of sampling points from a constrained set in R^d where the constraints can only be accessed in a zero order fashion. They consider the specific situation where the constraints are a solution of a complicated PDE solver and hence the derivatives or specific functional forms of the constraint cannot be obtained. They repose the problem as sampling from a Gibbs distribution whose potential contains constraints as penalties in a Lagrangian fashion. They now wish to sample from the Gibbs distribution using Langevin diffusion.  The Langevin process requires a derivative of the gradient. The setting does not allow for that and therefore the authors propose two approaches - \n\n1. Constructing the gradient from zeroth order entries of a gaussian smoothed potential (much like works of Nesterov et al on zero order optimization). \n2. Using a parameteric function class (like an RKHS or a neural network) to learn a function which well approximates the gradient of the constraints as well given zeroth order constraint evaluations. \n\n\nThe theoretical analysis seems quite rigorous as it begins by first providing a basic guarantee for constrained langevin sampling when gradients are computed with error. The non error gradient part of this analysis has been established before and the authors mention the references appropriately. I have a couple of questions regarding the precise statements of the theorem that i will ask towards the end of the review. Overall its hard to comment on the tightness of the analysis as the non-error versions are also unclear of the tightness of the bounds. Nevertheless the bounds achieved do not look much worse than the non-error counterparts and are easy to implement. The rest of the bounds focus on achieving low error in approximation of the gradients in various settings. Overall the theory in this part seems very loose in terms of bounds as exponential factors in dimensions start to appear and in that regard seems quite preliminary but its hard to comment on whether its natural or can be improved. \n\nSome questions I have regarding the theorem statements \n\nRegarding theorem 1 SPLMC convergence  (and corollaries of the theorem) -  I find it surprising that there is no lower bound assumption on eta in terms of K - only an upper bound. This seems wrong particularly as the theorem as stated then allows eta to be set extremely small while K is finite, in which case there should be no convergence theorems at all. The condition on eta should be a theta(f (K)) for some f type of condition like in the second part of the theorem. I would suggest the authors to look into the theorem - or claify why this is the case. \n\nI am confused by the presentation of the Shi et al results as there is no penalty appearing for the approximation error due to an RKHS, only a finite sample penalty. Does the result assume that phi belongs to the function class of the RKHS in question? Probably yes and in that case that should be specified. \n\n6: Weak Accept", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 510 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Review of \"Constrained Langevin Dynamics for Black-Box Optimization\"\n\nIntroduction:\n\nThe paper \"Constrained Langevin Dynamics for Black-Box Optimization\" presents a novel approach to sampling from a Gibbs distribution defined on a compact support, which is a fundamental problem in machine learning and optimization. The authors propose a constrained Langevin dynamics method that leverages surrogate modeling and gradient-free optimization to efficiently sample from the target distribution. In this review, I will provide an overview of the paper, highlight the key contributions and limitations, and suggest some potential directions for future research.\n\nOverview of the Paper:\n\nThe paper is organized into four main sections. The first section introduces the problem of sampling from a Gibbs distribution under constraints and discusses the challenges of black-box optimization. The second section reviews the background on Langevin dynamics and constrained optimization. The third section presents the proposed method, which consists of two steps: learning the gradient of the potential of the Gibbs distribution using deep neural networks or reproducing kernel spaces, and using the surrogate gradient model in the constrained Langevin dynamics. The fourth section provides numerical results on the effectiveness of the proposed method for the design of nanoporous configurations with improved thermoelectric efficiency.\n\nKey Contributions:\n\n1. The paper introduces a novel approach to constrained black-box optimization using surrogate model-based Langevin dynamics.\n2. The proposed method combines gradient-free optimization with deep neural networks or reproducing kernel spaces to learn the gradient of the potential of the Gibbs distribution.\n3. The paper provides numerical results demonstrating the effectiveness of the proposed method for the design of nanoporous configurations with improved thermoelectric efficiency.\n\nLimitations and Questions:\n\n1. The paper focuses on the case of a single constraint, and it is not clear how the method can be extended to multiple constraints or more complex objective functions.\n2. The authors use deep neural networks to learn the gradient of the potential, but it is not clear how other types of surrogate models, such as Gaussian processes or radial basis functions, might perform.\n3. The paper does not provide a thorough analysis of the convergence properties of the proposed method, and it is not clear how the method scales with the number of constraints or the size of the", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "This work addresses continual learning for non-stationary data, using Bayesian neural networks and memory-based online variational Bayes. We represent the posterior approximation of the network weights by a diagonal Gaussian distribution and a complementary memory of raw data. This raw data corresponds to likelihood terms that cannot be well approximated by the Gaussian. We introduce a novel method for sequentially updating both components of the posterior approximation. Furthermore, we propose Bayesian forgetting and a Gaussian diffusion process for adapting to non-stationary data. The experimental results show that our update method improves on existing approaches for streaming data. Additionally, the adaptation methods lead to better predictive performance for non-stationary data.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 109 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure! Here is the Abstract section for the paper you provided:\n\nAbstract:\n\nThis paper proposes a novel approach to online variational Bayesian inference for non-stationary data, which combines the advantages of both online and offline learning methods. The proposed method, called Approximate Bayesian Online Learning (ABOL), uses a diagonal Gaussian distribution and a running memory to approximate the posterior distribution of the model parameters. The memory is updated sequentially, while adapting to changes in the data distribution. The proposed method is evaluated on several popular datasets and shows improved performance compared to existing methods. The ABOL method provides a flexible and efficient way to perform online Bayesian inference for non-stationary data, which is particularly useful in applications such as energy demand forecasting, climate analysis, and user behavior analytics.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Streaming variational bayes , Authors: T. Broderick N. Boyd... , 2013\n2.  Title: Online variational Bayesian learning , Authors: Z. Ghahramani , 2000\n3.  Title: Overcoming catastrophic forgetting in neural networks , Authors: J. Kirkpatrick R. Pascanu... , 2017\n4.  Title: A practical Bayesian framework for backpropagation networks , Authors: D.J.C. MacKay , 1992\n5.  Title: Stochastic Models, Estimation and Control. Mathematics in science and engineering , Authors: P.S. Maybeck , 1982\n6.  Title: Virtual Vector Machine for Bayesian online classification , Authors: T.P. Minka R. Xiang... , 2009\n7.  Title: Variational continual learning , Authors: C.V. Nguyen Y. Li... , 2018\n8.  Title: A Bayesian approach to on-line learning , Authors: M. Opper , 1998\n9.  Title: Connectionist models of recognition memory: constraints imposed by learning and forgetting functions , Authors: R. Ratcliff , 1990\n10.  Title: Online structured laplace approximations for overcoming catastrophic forgetting , Authors: H. Ritter A. Botev... , 2018\n11.  Title: Catastrophic forgetting, rehearsal and pseudorehearsal , Authors: A. Robins , 1995\n12.  Title: Online model selection based on the variational bayes , Authors: M. Sato , 2001\n13.  Title: Continual learning with deep generative replay , Authors: H. Shin J.K. Lee... , 2017\n", "gt": "There are many Bayesian approaches to online learning, which differ mostly in the approximation of the posterior distribution at each time-step. Sequential Monte Carlo (Liu & Chen, 1998) approximates the posterior by a set of particles. Assumed Density Filtering (ADF) (Maybeck, 1982) and Bayesian online learning (Opper, 1998) are deterministic posterior approximations based on moment matching. Other deterministic approaches are based on Laplace\u2019s approximation (MacKay, 1992): Kirkpatrick et al. (2017) use multiple diagonal Gaussian posterior approximations of previous time-steps to regularise future tasks; Ritter et al. (2018) use a single (block-diagonal) posterior approximation, summarising all previous time-steps. The latter method is closer to Bayesian online inference, as it is an approximation of Eq. (1). Our work is based on online VB (Opper, 1998; Ghahramani, 2000; Sato, 2001; Broderick et al., 2013), which approximates the posterior at every time-step by minimising the KL-divergence between a parametric (here Gaussian) and the true posterior distribution. In contrast to online VB, we approximate the posterior by a Gaussian distribution and a running memory. Other approaches are based on various types of episodic memory, motivated by their empirical success in preventing catastrophic forgetting. The basic idea of rehearsal (Ratcliff, 1990) is to train on both the new data and a subset of previous data or pseudo samples (Robins, 1995; Shin et al., 2017; Kemker & Kanan, 2017) sampled from a generative model. The memory-based online inference methods most similar to our approach are VCL (Nguyen et al., 2018) and VVM (Minka et al., 2009). Both methods use a Gaussian distribution and a running memory to approximate the posterior. VCL uses heuristics such as random selection or the k-center method to update the memory.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 278 words.[/INST]", "llama2-13b-chat-4k_pred": " Related Work:\n\nThe paper \"Continual Learning with Approximate Bayesian Inference\" by Nguyen et al. (2018) proposes a method for continual learning that combines the strengths of both online and batch learning. The authors propose an approximate Bayesian approach that uses a Gaussian distribution to model the posterior distribution over the model parameters. This approach allows for efficient inference and learning in non-stationary data streams.\n\nSeveral other papers have also proposed methods for continual learning, including the Virtual Vector Machine (VVM) (Minka et al., 2009), which uses a Gaussian distribution to model the posterior distribution over the model parameters. Other papers have proposed methods that use a running memory of previous data points to improve the accuracy of the model (Robins, 1995; Lopez-Paz & Ranzato, 2017; Shin et al., 2017; Kamra et al., 2017).\n\nOne paper that is particularly related to the current paper is \"Online Variational Bayesian Learning\" by Ghahramani (2000), which proposes a method for online learning that uses a variational distribution to approximate the true posterior distribution. The author shows that this approach can be used to learn from non-stationary data streams and that it can be applied to a variety of models, including neural networks.\n\nAnother related paper is \"Streaming Variational Bayes\" by Broderick et al. (2013), which proposes a method for streaming data that uses a variational distribution to approximate the true posterior distribution. The authors show that this approach can be used to learn from large-scale data streams and that it can be applied to a variety of models, including neural networks.\n\nOther related papers include \"Overcoming Catastrophic Forgetting in Neural Networks\" by Kirkpatrick et al. (2017), which proposes a method for continual learning that uses a rehearsal mechanism to prevent catastrophic forgetting. Another related paper is \"A Practical Bayesian Framework for Backpropagation Networks\" by MacKay (1992), which proposes a Bayesian approach to learning in neural networks.\n\nIn summary, the current paper \"Continual Learning with Approximate Bayesian Inference\"", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "1. Summary:\nThis proposes considers neural networks training with non-stationary streaming data. To address online inference, the paper uses variational online updates and a running memory (coreset) summarising the data points seen so far, as recently used in the variational continual learning approach. First, the paper identifies a gap in this recent approach that coreset point selection process does not take into account the approximation quality. The paper develops a sound procedure to select the memory points, essentially to identify \u201cdifficult\u201d data points under the current posterior and put them into the running memory. Second, to handle \u201cconcept drift\u201d, the paper considers imposing some decaying on the likelihood of past data and derives post-hoc variational posterior updates for this case. Two contributions are validated on some streaming regression and classification tasks.\n\n2. Opinion and rationales\n\nI\u2019m leaning towards \u201caccept\u201d for this paper since it presents two interesting contributions (albeit of incremental novelty)  to the approximate inference area, it has clear execution and super clean presentation, and the experiments clearly demonstrate the values of the proposed approaches.\n\nI would like the paper to clarify the following:\n\na. I find the objective for selecting memory points interesting, but am wondering if using individual predictability terms by themselves are good enough to select these points. Perhaps, after some thoughts, memory is perhaps not the right word to characterise these points. \n\nIt seems to me (i) these terms indicate how well the current posterior predicts the data points and thus this objective will tend to favour points with low predictive likelihood to be selected. That is, these points are important when *all other points* are presented, (ii) there will be *no diversity* in the memory as illustrated in the 2d classification example.\n\nThis means the points selected here have quite different characteristics compared to coreset points or the full dataset in general. Coreset points ideally can compactly represent the full dataset and can be used for inference in place of the full dataset.\n\nThat said, the contribution presented here is very useful. Just that I\u2019m not sure how well this will work in more challenging continual learning set-up where diversity is important for a long sequence of diverse tasks.\n\nb. The paper presents a post-hoc modification to approximate posterior (\u2018s mean and variance) to account for the decay in the likelihood contribution from past data. I wonder if this post-hoc adjustment is identical to running approximate inference with the adjusted likelihood?\n\nc. the adaptation parameters need to be tuned/known in advance, which seems to be a disadvantage of the current approach. Is the update presented here somewhat robust to mis-specification of these parameters? How would these be tuned in a more practical setting in which data arrive sequentially and we might not know the underlying \u201cconcept\u201d.\n\n6: Weak Accept", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 465 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Review of \"Continual Learning with Non-stationary Streaming Data\"\n\nIntroduction:\n\nThe paper \"Continual Learning with Non-stationary Streaming Data\" presents a novel approach to address the challenges of continual learning in the context of non-stationary streaming data. The authors propose an approximate Bayesian approach that leverages a running memory of previous data to improve the adaptability of the model to changes in the data distribution. In this review, I will provide an overview of the paper, highlight the key contributions and limitations, and suggest some potential directions for future research.\n\nOverview of the paper:\n\nThe paper is organized into four main sections. The first section provides an introduction to the problem of continual learning and the challenges of non-stationary streaming data. The authors then propose their approach, which consists of an online approximation and two alternative adaptation methods. The third section describes the experimental results of the proposed approach on several popular datasets. The final section discusses the related work and concludes the paper.\n\nKey contributions:\n\n1. The authors propose an approximate Bayesian approach for continual learning with non-stationary streaming data, which leverages a running memory of previous data to improve the adaptability of the model to changes in the data distribution.\n2. The approach is based on a diagonal Gaussian distribution and a complementary memory of previous data, which allows for efficient inference and adaptation to changes in the data distribution.\n3. The authors propose two alternative adaptation methods, which generalize the online variational Bayes with Bayesian neural networks to non-stationary data.\n\nLimitations:\n\n1. The proposed approach relies on the assumption of i.i.d. data, which may not always hold in real-world applications.\n2. The approach is based on a Gaussian distribution, which may not be the most appropriate distribution for modeling the data.\n3. The authors do not provide a thorough evaluation of the approach on non-stationary data, which limits the understanding of its effectiveness in real-world scenarios.\n\nSuggestions and questions:\n\n1. How does the proposed approach perform on non-i.i.d. data? Are there any modifications or extensions to the approach that can handle non-i.i.d. data?\n2. Can the approach be applied to other types of data, such as time-series or image data", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Recurrent Neural Networks (RNNs) are very successful at solving challenging problems with sequential data. However, this observed efficiency is not yet entirely explained by theory. It is known that a certain class of multiplicative RNNs enjoys the property of depth efficiency \u2014 a shallow network of exponentially large width is necessary to realize the same score function as computed by such an RNN. Such networks, however, are not very often applied to real life tasks. In this work, we attempt to reduce the gap between theory and practice by extending the theoretical analysis to RNNs which employ various nonlinearities, such as Rectified Linear Unit (ReLU), and show that they also benefit from properties of universality and depth efficiency. Our theoretical results are verified by a series of extensive computational experiments.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 130 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure, here is the Abstract section for the paper you provided:\n\nAbstract:\n\nThis paper explores the connection between tensor decompositions and recurrent neural networks (RNNs) with rectifier nonlinearities. The authors show that the score functions for tensor train (TT) and tensor network (TN) decompositions exhibit a particular recurrent structure similar to that of RNNs. They also introduce a generalized outer product that allows for more practical nonlinearities and analyze the corresponding networks. The results provide a deeper understanding of the expressive power of RNNs and their relationship to tensor decompositions, and may lead to the development of more efficient and effective deep learning techniques.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: On the expressive power of deep learning: A tensor analysis , Authors: Nadav Cohen Or Sharir... , 2016\n2.  Title: Boosting dilated convolutional networks with mixed tensor decompositions , Authors: Nadav Cohen Ronen Tamari... , 2018\n3.  Title: Hierarchical singular value decomposition of tensors , Authors: Lars Grasedyck , 2029\n4.  Title: Foundations of the PARAFAC procedure: Models and conditions for an \u201dexplanatory\u201d multimodal factor analysis , Authors: Richard A Harshman , 1970\n5.  Title: Expressive power of recurrent neural networks , Authors: Valentin Khrulkov Alexander Novikov... , 2018\n6.  Title: Speedingup convolutional neural networks using fine-tuned cp-decomposition , Authors: Vadim Lebedev Yaroslav Ganin... , 2015\n7.  Title: Tensorizing neural networks , Authors: Alexander Novikov Dmitrii Podoprikhin... , 2015\n8.  Title: Tensor-train decomposition , Authors: Ivan V Oseledets , 2011\n9.  Title: On multiplicative integration with recurrent neural networks , Authors: Yuhuai Wu Saizheng Zhang... , 2016\n10.  Title: Tensor-train recurrent neural networks for video classification , Authors: Yinchong Yang Denis Krompass... , 2017\n11.  Title: Long-term forecasting using tensor-train RNNs , Authors: Rose Yu Stephan Zheng... , 2017\n", "gt": "Tensor methods have a rich history of successful application in machine learning. (Vasilescu & Terzopoulos, 2002), in their framework of TensorFaces, proposed to treat facial image data as multidimensional arrays and analyze them with tensor decompositions, which led to significant boost in face recognition accuracy. (Bailey & Aeron, 2017) employed higher-order co-occurence data and tensor factorization techniques to improve on word embeddings models. Tensor methods also allow to produce more accurate and robust recommender systems by taking into account a multifaceted nature of real environments (Frolov & Oseledets, 2017). In recent years a great deal of work was done in applications of tensor calculus to both theoretical and practical aspects of deep learning algorithms. (Lebedev et al., 2015) represented filters in a convolutional network with CP decomposition (Harshman, 1970; Carroll & Chang, 1970) which allowed for much faster inference at the cost of a negligible drop in performance. (Novikov et al., 2015) proposed to use Tensor Train (TT) decomposition (Oseledets, 2011) to compress fully\u2013connected layers of large neural networks while preserving their expressive power. Later on, TT was exploited to reduce the number of parameters and improve the performance of recurrent networks in long\u2013term forecasting (Yu et al., 2017) and video classification (Yang et al., 2017) problems. In addition to the practical benefits, tensor decompositions were used to analyze theoretical aspects of deep neural nets. (Cohen et al., 2016) investigated a connection between various network architectures and tensor decompositions, which made possible to compare their expressive power. Specifically, it was shown that CP and Hierarchial Tucker (Grasedyck, 2010) decompositions correspond to shallow networks and convolutional networks respectively. Recently, this analysis was extended by (Khrulkov et al., 2018) who showed that TT decomposition can be represented as a recurrent network with multiplicative connections. This specific form of RNNs was also empirically proved to provide a substantial performance boost over standard RNN models (Wu et al., 2016). First results on the connection between tensor decompositions and neural networks were obtained for rather simple architectures, however, later on, they were extended in order to analyze more practical deep neural nets. It was shown that theoretical results can be generalized to a large class of CNNs with ReLU nonlinearities (Cohen & Shashua, 2016) and dilated convolutions (Cohen et al., 2018), providing valuable insights on how they can be improved.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 385 words.[/INST]", "llama2-13b-chat-4k_pred": " Related Work:\n\nThe paper \"On the Expressive Power of Deep Learning: A Tensor Analysis\" by Nadav Cohen, Or Sharir, and Yonina C. Eldar, published in 2016, provides a comprehensive analysis of the expressive power of deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The authors show that these networks can efficiently represent a rich class of functions with a relatively small number of parameters, but the tools used for analyzing these networks are not yet fully understood.\n\nIn the context of RNNs, the paper \"Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions\" by Nadav Cohen, Ronen Tamari, and Yonina C. Eldar, published in 2018, introduces a new technique called mixed tensor decompositions to boost the performance of dilated convolutional networks. The authors show that these decompositions can be used to efficiently represent the weights of the network and improve its expressive power.\n\nAnother relevant paper is \"Hierarchical Singular Value Decomposition of Tensors\" by Lars Grasedyck, published in 2029, which provides a comprehensive analysis of the hierarchical singular value decomposition (HSVD) of tensors. The author shows that HSVD can be used to efficiently represent high-order tensors and provide insights into the structure of the data.\n\nThe paper \"Foundations of the PARAFAC procedure: Models and conditions for an 'explanatory' multimodal factor analysis\" by Richard A. Harshman, published in 1970, provides a comprehensive analysis of the PARAFAC procedure, a method for factor analysis of multimodal data. The author shows that the PARAFAC procedure can be used to identify the underlying factors of the data and provide insights into the structure of the data.\n\nThe paper \"Expressive Power of Recurrent Neural Networks\" by Valentin Khrulkov, Alexander Novikov, and Dmitri Podoprikhin, published in 2018, provides a comprehensive analysis of the expressive power of RNNs. The authors show that RNNs can efficiently represent a rich class of functions and provide insights into", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The authors extend the theoretical results of a paper previously presented in the last edition of ICLR (2018), where it was demonstrated that Recurrent Neural Network can be interpreted as a tensor network decomposition based on the Tensor-Train (TT, Oseledets et al, 2011).\n\nThe paper is well written and have a good structure. However, I found that some deep concepts are not well introduced, and maybe other more trivial results are discussed with unnecessary details. The following comments could help authors to improve the quality of presentation of their paper:\n-\tSection 3.1 (Score Functions and Feature Tensor) is a bit short and difficult to read. \no\tMaybe, a more motivating introduction could be included in order to justify the definition of score functions (eq. 2). \no\tIt would be also nice to state that, according to eq. (3), the feature tensor is a rank-1 tensor. \no\tI would suggest moving the definition of outer product to the Appendix, since most readers know it very well.\no\tIt is said that eq. 2 possesses the universal approximation property (it can approximate any function with any prescribed precision given sufficiently large M). It is not clear which is the approximation function.\n-       When scores functions are presented (eq. 2) it is written the term \"logits\" between brackets. Could you please clarify why this term is introduced here? Usually, logit of a probability p is defined as L(p)=p/(1-p). What is the usage of this term in this work? \n-      I think the theory is presented for a model with the two-classes only but used for multiple classes in the experimental sections. It should be necessary to make some comment about this in the paper.\n-      Details about how the RNN based on TT is applied must be added. More specifically, the authors should provide answers to clarify the following questions: \n(i) Are patches overlapped or non-overlapped? \n(ii) What value of M is used? and is there any general rule for this choice? \n(iii) How the classification in the 10-classes is obtained? Are you using a softmax function in the last layer? Are you using one weight tensor W_c per class (c=1,2,...,10). Please provide these technical details. \n(iv) Please, specify which nonlinear activation sigma is used in the feature map f_\\theta(x).\n(v) How many feature maps are used? and, Are the matrix A and vector b learned from training dataset or only the TT-cores need to be learned? \n7: Good paper, accept", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 409 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Review of \"Recurrent Neural Networks with Tensor Train Decompositions\"\n\nIntroduction:\n\nThe paper \"Recurrent Neural Networks with Tensor Train Decompositions\" presents a novel approach to deep learning by combining Tensor Train (TT) decompositions with Recurrent Neural Networks (RNNs). The authors aim to improve the efficiency and expressiveness of RNNs by leveraging the advantages of TT decompositions. In this review, I will provide an overview of the paper, highlight its key contributions, and offer some suggestions and questions for future research.\n\nOverview of the Paper:\n\nThe paper begins with an introduction to TT decompositions and their applications in deep learning. The authors then introduce their proposed method, which combines TT decompositions with RNNs. The method is based on the idea of representing the weight tensor of an RNN as a TT decomposition, which allows for more efficient and expressive representations. The authors demonstrate the effectiveness of their method on several benchmark datasets and compare it with other state-of-the-art methods.\n\nKey Contributions:\n\n1. The paper introduces a novel approach to deep learning by combining TT decompositions with RNNs, which improves the efficiency and expressiveness of RNNs.\n2. The authors propose a method for representing the weight tensor of an RNN as a TT decomposition, which allows for more efficient and expressive representations.\n3. The paper demonstrates the effectiveness of the proposed method on several benchmark datasets and compares it with other state-of-the-art methods.\n\nSuggestions and Questions:\n\n1. The paper focuses primarily on the theoretical aspects of the proposed method, and there is limited discussion on the practical implementation of the method. It would be interesting to see more practical applications of the method and its performance on real-world datasets.\n2. The authors mention that the proposed method can be applied to various types of RNNs, but they only demonstrate it on simple RNNs. It would be useful to explore the application of the method on more complex RNN architectures, such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs).\n3. The paper does not provide a comprehensive comparison of the proposed method with other state-of-the-art methods", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Simultaneous machine translation models start generating a target sequence before they have encoded the source sequence. Recent approaches for this task either apply a fixed policy on a state-of-the art Transformer model, or a learnable monotonic attention on a weaker recurrent neural network-based structure. In this paper, we propose a new attention mechanism, Monotonic Multihead Attention (MMA), which extends the monotonic attention mechanism to multihead attention. We also introduce two novel and interpretable approaches for latency control that are specifically designed for multiple attention heads. We apply MMA to the simultaneous machine translation task and demonstrate better latency-quality tradeoffs compared to MILk, the previous state-of-the-art approach. We analyze how the latency controls affect the attention span and we study the relationship between the speed of a head and the layer it belongs to. Finally, we motivate the introduction of our model by analyzing the effect of the number of decoder layers and heads on quality and latency.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 157 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure, here is the Abstract section for the paper you provided:\n\nAbstract:\n\nThis paper proposes a novel approach to simultaneous machine translation, called Monotonic Multihead Attention (MMA), which combines the expressive power of multihead attention and the low latency of monotonic attention. MMA is designed to address the limitations of previous monotonic attention approaches, which are based on RNN encoder-decoder models and have not explored the power of the Transformer model. The proposed model leverages the Transformer architecture and introduces two variants, MMA-H and MMA-IL, which differ in their attention span and quality/latency tradeoffs. The authors also propose two latency regularization methods to control the attention span and maintain a balance between quality and latency. The experimental results on two standard translation benchmarks show that MMA outperforms the previous state-of-the-art model, MILk, and achieves better tradeoffs between quality and latency. The analysis on the relationship between the speed of a head and the layer it belongs to provides insights into the design of the model.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Incremental decoding and training methods for simultaneous translation in neural machine translation , Authors: Fahim Dalvi Nadir Durrani... , 2018\n2.  Title: Don\u2019t until the final verb wait: Reinforcement learning for simultaneous machine translation , Authors: Alvin Grissom II He He... , 2014\n3.  Title: Learning to translate in real-time with neural machine translation , Authors: Jiatao Gu Graham Neubig... , 2017\n4.  Title: Learning hard alignments with variational inference , Authors: Dieterich Lawson Chung-Cheng Chiu... , 2018\n5.  Title: Learning online alignments with continuous rewards policy gradient , Authors: Yuping Luo Chung-Cheng Chiu... , 2017\n6.  Title: STACL: Simultaneous translation with implicit anticipation and controllable latency using prefix-to-prefix framework , Authors: Mingbo Ma Liang Huang... , 2019\n", "gt": "Recent work on simultaneous machine translation falls into three categories. In the first one, models use a rule-based policy for reading input and writing output. Cho & Esipova (2016) propose a WaitIf-* policy to enable an offline model to decode simultaneously. Ma et al. (2019) propose a wait-k policy where the model first reads k tokens, then alternates between read and write actions. Dalvi et al. (2018) propose an incremental decoding method, also based on a rule-based schedule. In the second category, a flexible policy is learnt from data. Grissom II et al. (2014) introduce a Markov chain to phrase-based machine translation models for simultaneous machine translation, in which they apply reinforcement learning to learn the read-write policy based on states. Gu et al. (2017) introduce an agent which learns to make decisions on when to translate from the interaction with a pre-trained offline neural machine translation model. Luo et al. (2017) used continuous rewards policy gradient for online alignments for speech recognition. Lawson et al.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 166 words.[/INST]", "llama2-13b-chat-4k_pred": "  Related Work:\n\nSeveral works have addressed simultaneous machine translation, focusing on incremental decoding and training methods (Dalvi et al., 2018), reinforcement learning (Grissom et al., 2014), real-time translation with neural machine translation (Gu et al., 2017), learning hard alignments with variational inference (Lawson et al., 2018), and online alignments with continuous rewards policy gradient (Luo et al., 2017).\n\nRecently, STACL (Ma et al., 2019) proposed a prefix-to-prefix framework for simultaneous translation with implicit anticipation and controllable latency. However, these works do not explore the power of the Transformer model or propose novel attention mechanisms like Monotonic Multihead Attention (MMA) to balance quality and latency.\n\nOur work addresses these limitations by introducing MMA, which combines the expressive power of multihead attention and the low latency of monotonic attention. MMA allows the model to attend to different positions and adjust latency on-the-fly, preserving both quality and history information.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper proposes a fully transformer-based monotonic attention framework that extends the idea of MILK. Though the idea of monotonic multi-head attention sounds interesting, I still have some questions below:\n\nAbout the method:\n   1. Is that possible that the MMA would have worse latency than MILK since all the attention heads need to agree to write while MILK only has one attention head?\n   2. Is there any attention order between different attention head?\n   3. I think the MMA only could control the latency during training time, which would produce different models with different latency. Is there any way that enables MMA to control the latency during inference time? Can we change the latency for on given model by tuning the requirements mentioned in (1)?\n\nAbout the experiments:\n    1. Do you have any explanation of why both MMA-H and MMA-IL have better BLEU when AL is small? The results in fig 2 seem counterintuitive. \n    2. I suggest the authors do more analysis of the difference between different attention heads to prove the effectiveness of MMA. \n    3. For the left two figures in fig 4, which one is the baseline, and which one is the proposed model?\n\nI also suggest the authors present more real sample analysis and discussions about the experiments.\n6: Weak Accept", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 213 words.[/INST]", "llama2-13b-chat-4k_pred": " The paper presents a novel approach to simultaneous machine translation, called Monotonic Multihead Attention (MMA), which combines the expressive power of multihead attention and the low latency of monotonic attention. The proposed model leverages the Transformer architecture and introduces two variants, MMA-H and MMA-IL, to balance quality and latency. The authors also propose two latency regularization methods to control the attention span and prevent the latency from being dominated by a single or a few heads.\n\nThe paper provides a thorough analysis of the proposed model and its variants, including the relationship between the speed of a head and the layer it belongs to. The authors also conduct experiments on two standard translation benchmarks, IWSLT15 English-Vietnamese and WMT15 German-English, and show that the proposed model outperforms the state-of-the-art MILk model in terms of both translation quality and latency.\n\nHowever, there are some suggestions and questions that can be raised:\n\n1. The paper does not provide a detailed comparison of the proposed model with other state-of-the-art models, such as the RNN-based models and the Transformer-based models without multihead attention. It would be interesting to see how the proposed model performs compared to these models.\n2. The authors use two latency regularization methods to control the attention span, but it is not clear how these methods are related to the monotonic attention mechanism. It would be useful to explore the relationship between these methods and the monotonic attention mechanism in more detail.\n3. The authors mention that the proposed model can be used for streaming systems, but it is not clear how well the model performs in real-time translation scenarios. It would be useful to conduct experiments on real-time translation tasks to evaluate the model's performance in these scenarios.\n4. The paper does not provide a detailed analysis of the impact of the number of decoder layers and the number of decoder heads on the performance of the proposed model. It would be interesting to explore the effect of these hyperparameters on the model's performance in more detail.\n\nOverall, the paper presents a novel approach to simultaneous machine translation that combines the expressive power of multihead attention and the low latency of monotonic attention. The proposed model shows promising results on two standard translation benchmarks, and the authors provide a thorough analysis", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Robustness is an important property to guarantee the security of machine learning models. It has recently been demonstrated that strong robustness certificates can be obtained on ensemble classifiers generated by input randomization. However, tight robustness certificates are only known for symmetric norms including `0 and `2, while for asymmetric norms like `1, the existing techniques do not apply. By converting the likelihood ratio into a one dimensional mixed random variable, we derive the first tight `1 robustness certificate under isotropic Laplace distributions in binary case. Empirically, the deep networks smoothed by Laplace distributions yield the state-of-the-art certified robustness in `1 norm on CIFAR-10 and ImageNet.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 105 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure, here is the abstract section for the paper you provided:\n\nAbstract:\n\nThis paper presents a novel approach to certified robustness for deep neural networks against adversarial attacks. The authors propose a new technique based on randomized smoothing and Neyman-Pearson lemma to derive tight robustness certificates for general norms. The approach is applied to binary and multi-class classification settings and shows superior performance compared to existing methods. The proposed method provides a new perspective on certified robustness and demonstrates its effectiveness in ensuring the security of deep learning models against adversarial attacks.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Sorting out lipschitz function approximation , Authors: Cem Anil James Lucas... , 2018\n2.  Title: A unified view of piecewise linear neural network verification , Authors: Rudy Bunel Ilker Turkaslan... , 2018\n3.  Title: Maximum resilience of artificial neural networks , Authors: Chih-Hong Cheng Georg N\u00fchrenberg... , 2017\n4.  Title: Parseval networks: Improving robustness to adversarial examples , Authors: Moustapha Cisse Piotr Bojanowski... , 2017\n5.  Title: Output range analysis for deep neural networks , Authors: Souradeep Dutta Susmit Jha... , 2017\n6.  Title: Regularisation of neural networks by enforcing lipschitz continuity , Authors: Henry Gouk Eibe Frank... , 2018\n7.  Title: On the effectiveness of interval bound propagation for training verifiably robust models , Authors: Sven Gowal Krishnamurthy Dvijotham... , 2018\n8.  Title: Differentiable abstract interpretation for provably robust neural networks , Authors: Matthew Mirman Timon Gehr... , 2018\n9.  Title: Fast and effective robustness certification , Authors: Gagandeep Singh Timon Gehr... , 2018\n10.  Title: Evaluating robustness of neural networks with mixed integer programming , Authors: Vincent Tjeng Kai Y Xiao... , 2017\n11.  Title: Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks , Authors: Yusuke Tsuzuku Issei Sato... , 2018\n12.  Title: Enresnet: Resnet ensemble via the feynman-kac , Authors: Bao Wang Binjie Yuan... , 2018\n13.  Title: Towards fast computation of certified robustness for relu networks , Authors: Tsuiwei Weng Huan Zhang... , 2018\n14.  Title: Efficient neural network robustness certification with general activation functions , Authors: Huan Zhang Tsui-Wei Weng... , 2018\n", "gt": "Robustness of a model can be defined in various aspects. For example, Feynman-Kac Formalism can be used to improve robustness (Wang et al., 2018). In this paper, we focus on the classification setting, where the goal is to provide guarantee of a constant prediction among a small region specified via some metric. The robustness certificate can be either exact or conservative, so long as a constant prediction is guaranteed in the certified region. Note that the certification of a completely black-box model requires checking the prediction values at every point around the point of interest, which is clearly infeasible. A practical certification algorithm inevitably has to specify and leverage the functional structure of the classifier in use to reduce the required computation. Exact certificates. The exact certificate of deep networks is typically derived for the networks with a piecewise linear activation function such as ReLU. Such networks have an equivalent mixed integer linear representation (Cheng et al., 2017; Lomuscio & Maganti, 2017; Dutta et al., 2017; Bunel et al., 2018). Hence, one may apply mixed integer linear programming to find the worst case adversary within any convex polyhedron such as an `1-ball or `\u221e-ball. Despite the elegant solution, the complexity is, in general, NP-hard and the algorithms are not scalable to large problems(Tjeng et al., 2017). Conservative certificates. A conservative certificate can be more scalable than the exact methods, since one can trade-off the accuracy of certification with efficiency (Gouk et al., 2018; Tsuzuku et al., 2018; Cisse et al., 2017; Anil et al., 2018; Hein & Andriushchenko, 2017). For example, one can relax the search of the worst case adversary as a simpler optimization problem that only bounds the effect of such adversary. Alternatively, people also consider the robustness problem in a modular way, where the robustness guarantee can be derived iteratively for each layer in the deep networks by considering the feasible values for each hidden layer (Gowal et al., 2018; Weng et al., 2018; Zhang et al., 2018; Mirman et al., 2018; Singh et al., 2018).", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 339 words.[/INST]", "llama2-13b-chat-4k_pred": " Sure, I'd be happy to help! Here's the Related Work section for the paper you provided, with proper citations:\n\nRelated Work\n\nSeveral works have addressed the problem of certifying the robustness of deep neural networks. Cohen et al. (2019) proposed a method for generating ensembles of a fixed black-box classifier based on input randomization, which leads to tight robustness certificates. However, their method is limited to specific attack algorithms and does not provide a general framework for certifying robustness against adversarial attacks.\n\nLemoy et al. (2019) proposed a method for verifying the robustness of neural networks using interval bound propagation. Their method is effective in training verifiably robust models, but it is computationally expensive and limited to small-scale datasets.\n\nGowal et al. (2018) proposed a method for regularizing neural networks by enforcing Lipschitz continuity, which leads to improved robustness against adversarial attacks. However, their method is limited to specific architectures and does not provide a general framework for certifying robustness.\n\n\nSeveral works have also addressed the problem of evaluating the robustness of neural networks using output range analysis. Dutta et al. (2017) proposed a method for evaluating the robustness of neural networks using output range analysis, which leads to improved robustness against adversarial attacks. However, their method is limited to specific attack algorithms and does not provide a general framework for certifying the robustness of neural networks.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "In this paper, the author derived a tight ell_1, which is not the symmetric norm, robustness certificates under isotropic Laplace distributions. Experimentally, the authors showed that the deep networks smoothed \nby Laplace distributions yield the state-of-the-art certified robustness in ell_1 norm on the CIFAR-10 \nand ImageNet. To find the ell_1 certificate, the authors first identified the tight robustness certificate, for attacking the model in one particular direction, say the first direction. To show that any other perturbation directions cannot lead to a worse result, the authors convert the d dimensional likelihood function into a one-dimensional function, and the authors used relaxation for different perturbations and show that the worst-case result is bounded by the previously identified direction.  However, I have the following concerns about this work:\n\n1. Theoretically, the authors only showed the certificate is tight for binary classification. I would suggest\nthe author change their claim in the abstract.\n\n2. What is M on page 3 which is used without definition after definition 1?\n\n3. Can you give a concrete continuous probability distribution that leads to the scenario in Fig.~3?\n\n4. Can you extend the analysis to a multi-class classification scenario?\n\n5. Besides randomized smoothing on the input images, recently Wang et al showed that randomize the deep nets can\nalso improve the deep nets and they gave it a nice theoretical interpretation. Here is the reference: Bao Wang, Binjie Yuan, Zuoqiang Shi, Stanley J. Osher. ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies, arXiv:1811.10745, NeurIPS, 2019\n\nOverall, since this work is a straightforward integration of some existing work, I think this\npaper lack novelty. Please address the above questions in rebuttal.\n3: Weak Reject", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 280 words.[/INST]", "llama2-13b-chat-4k_pred": " Review of \"Certified Robustness of Deep Neural Networks against Adversarial Examples\"\n\nThe paper \"Certified Robustness of Deep Neural Networks against Adversarial Examples\" presents a novel approach to establishing the robustness of deep neural networks against adversarial attacks. The authors propose a method based on randomized smoothing and Neyman-Pearson lemma to certify the robustness of a given model against worst-case attacks. The approach is evaluated on several benchmark datasets, and the results show that the proposed method outperforms existing techniques in terms of tightness and computational efficiency.\n\nOne of the main contributions of the paper is the introduction of a new bound for the robustness certificate, which is tighter than previous bounds for the binary classification case. The authors also provide a thorough analysis of the bound and demonstrate its tightness through a series of experiments. Furthermore, the authors extend their approach to the multi-class classification setting and show that their method is always tighter than the previous certificate proposed by Lecuyer et al. (2019).\n\nOne suggestion I have is to provide more interpretability of the results. While the authors show that their method outperforms existing techniques, they do not provide a clear understanding of how and why their method works better. Additionally, the authors do not provide a thorough analysis of the limitations of their approach, which could be useful for future research.\n\nAnother suggestion is to explore other applications of the proposed method beyond adversarial attacks. For example, the method could be applied to other types of attacks, such as data poisoning or model inversion attacks. Additionally, the method could be used to improve the generalization ability of deep neural networks to unseen data.\n\nFinally, I have some questions regarding the practical applicability of the proposed method. Firstly, the method requires the knowledge of the attack distribution, which may not be available in practice. Secondly, the method is based on the assumption that the attack is worst-case, which may not always be the case in real-world scenarios. Finally, the method is computationally expensive, which may limit its applicability to large-scale datasets.\n\nIn conclusion, the paper \"Certified Robustness of Deep Neural Networks against Adversarial Examples\" presents a novel approach to establishing the robustness of deep neural networks against adversarial attacks. The proposed method outperforms existing techniques in", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention thanks to its encouraging performance on a variety of control tasks. Yet, conventional regularization techniques in training neural networks (e.g., L2 regularization, dropout) have been largely ignored in RL methods, possibly because agents are typically trained and evaluated in the same environment. In this work, we present the first comprehensive study of regularization techniques with multiple policy optimization algorithms on continuous control tasks. Interestingly, we find conventional regularization techniques on the policy networks can often bring large improvements on the task performance, and the improvement is typically more significant when the task is more difficult. We also compare with the widely used entropy regularization and find L2 regularization is generally better. Our findings are further confirmed to be robust against the choice of training hyperparameters. We also study the effects of regularizing different components and find that only regularizing the policy network is typically the best option. We hope our study provides guidance for future practices in regularizing policy optimization algorithms.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 172 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure! Here is the abstract section for the paper you provided:\n\nAbstract:\n\nRegularization techniques are crucial in deep reinforcement learning to prevent overfitting and improve generalization. However, these techniques are largely absent or underutilized in deep reinforcement learning. In this work, we investigate the effectiveness of common regularization methods in policy optimization algorithms, which have been largely ignored in the reinforcement learning literature. We find that conventional regularizations can significantly improve the performance of policy optimization algorithms on continuous control tasks, especially on harder tasks. Remarkably, L2 regularization generally outperforms entropy regularization, and dropout and Batch Normalization tend to bring improvements only on off-policy algorithms. Our results suggest that regularization methods can be as important as high-level reinforcement learning algorithms in terms of boosting performance. We provide the first comprehensive study of regularization methods in policy optimization algorithms, and our findings have important implications for the development of deep reinforcement learning algorithms.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Control regularization for reduced variance reinforcement learning , Authors: Richard Cheng Abhinav Verma... , 1905\n2.  Title: Addressing function approximation error in actorcritic methods , Authors: Scott Fujimoto Herke van Hoof... , 2018\n3.  Title: Imformation asymmetry in kl-regularized rl , Authors: Alexandre Galashov Siddhant Jayakumar... , 2019\n4.  Title: Continuous control with deep reinforcement learning , Authors: Timothy Lillicrap Jonathan Hunt... , 2016\n5.  Title: Continuous control with deep reinforcement learning , Authors: Timothy P Lillicrap Jonathan J Hunt... , 2015\n6.  Title: Td-regularized actor-critic methods , Authors: Simone Parisi Voot Tangkaratt... , 2019\n7.  Title: Trust region policy optimization , Authors: John Schulman Sergey Levine... , 2015\n8.  Title: High-dimensional continuous control using generalized advantage estimation , Authors: John Schulman Philipp Moritz... , 2016\n9.  Title: Proximal policy optimization algorithms , Authors: John Schulman Filip Wolski... , 2017\n", "gt": "Regularization in Deep RL. Conventional regularization methods have rarely been applied in deep RL. One rare case of such use is in Deep Deterministic Policy Gradient (DDPG) (Lillicrap et al., 2016), where Batch Normalization is applied to all layers of the actor \u00b5 network and layers of the critic Q network prior to the action input, and L2 regularization is applied to the critic Q network because it tends to have overestimation bias (Fujimoto et al., 2018). Some recent studies have developed more complicated regularization approaches to continuous control tasks. Cheng et al. (2019) regularizes the stochastic action distribution \u03c0(a|s) using a suboptimal control prior. The regularization weight at a given state is adjusted based on the temporal difference (TD) error. The larger the TD error, the more the action distribution moves towards the prior. Galashov et al. (2019) introduces a default policy that receives limited information as a regularizer. The information asymmetry between the behavior policy and the default policy helps to accelerate convergence and improve performance. Parisi et al. (2019) introduces TD error regularization to penalize inaccurate value estimation and Generalized Advantage Estimation (GAE) (Schulman et al., 2016) regularization to penalize GAE variance. However, most of these regularizations are rather complicated (Galashov et al., 2019), specifically designed for certain algorithms (Parisi et al., 2019), or need prior information (Cheng et al., 2019). Also, these techniques consider regularizing the output\nof the network, while conventional regularization methods mostly directly regularize the parameters.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 243 words.[/INST]", "llama2-13b-chat-4k_pred": " Related Work:\n\nThe regularization of deep reinforcement learning (RL) has been a topic of interest in recent years. In this section, we will discuss related works that have investigated the use of regularization techniques in RL, particularly in the context of continuous control tasks.\n\nOne of the earliest works on regularization in RL is the study by Cheng et al. (2015) on control regularization for reduced variance reinforcement learning. The authors proposed a regularization method that adds a control term to the policy gradient algorithm to reduce the variance of the policy gradient estimate. This work laid the foundation for the use of regularization techniques in RL.\n\nAnother important work in this area is the study by Fujimoto et al. (2018) on addressing function approximation error in actor-critic methods. The authors proposed a regularization method that adds a term to the actor-critic objective function to encourage the use of the entire state space. This work demonstrated the effectiveness of regularization in improving the performance of actor-critic methods.\n\nGalashov et al. (2019) proposed a regularization method based on the Kullback-Leibler (KL) divergence to address the issue of information asymmetry in RL. The authors showed that the KL regularization term can be used to improve the exploration-exploitation trade-off in RL.\n\nLillicrap et al. (2016) proposed a deep reinforcement learning algorithm for continuous control tasks, which was later improved by Hunt et al. (2018) with the use of a trust region optimization method. These works demonstrated the effectiveness of deep RL in solving continuous control tasks.\n\nParisi et al. (2019) proposed a regularization method based on the TD-regularized actor-critic method. The authors showed that the TD regularization term can be used to improve the stability and convergence of actor-critic methods.\n\nSchulman et al. (2015) proposed a trust region policy optimization algorithm, which was later improved by Schulman et al. (2017) with the use of a proximal policy optimization method. These works demonstrated the effectiveness of policy optimization methods in solving continuous control tasks.\n\nFinally, Tangkar", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The authors present that regularization methods mostly help to improve the agents' performance in terms of final scores. Specifically, they show that direct regularizations on model parameters, such as the standard case of L2 or L1 regularization, generally improve the agent performance. They also show that these regularizations, in their study, is more proper than entropy regularization. The authors also show that, in the presence of such regularizations, the learning algorithms become less sensitive to the hyperparameters. \n\nFew comments:\n1) The paper is well written and easy to follow. I appreciate it. I found the writing of the paper has a bit of repetition. The authors might find it slightly more proper to remove some of the repetitions (e.g. section 4.2)\n2) I would suggest rephrasing the last two sentences of the second paragraph in related work: \"Also, these techniques consider ...\". Regularizing the output also regularizes the parameters, I think the authors' point was \"directly regularize\" the parameters. \n3) In the \"Entropy Regularization\" part of section 3, I guess the Hs has not been defined. \n4) Repeated \"the\" in the last paragraph of section 4.1 (despite it already incorporates the the maximization of)\n5) The authors used the term \"not converge\" multiple times. While it is hard from the plots to see whether the series converges or not, I have a strong feeling that by this term the authors mean the algorithm does not converge to a resealable solution rather than being divergent up to a bandwidth. Maybe clarifying would be helpful.\n6) In section 5, the authors study the sensitivity to the hyperparameters. In this section, I had a hard time to understand the role of term 3\n\"BN and dropout hurts on-policy algorithms but can bring improvement only for the off-policy SAC algorithm.\" Does it mean that deploying BN, results in a more sensitive algorithm? or it means that the performance degrades (which is a different topic than section 5 is supposed to serve)?\n7) In section 7, the authors put out a hypothesis \"\nHowever, there is still generalization between samples: the agents are only trained on the limited\" but the provided empirical study might not fully be considered to be designed to test this hypothesis. In order to test this hypothesis, the author might be interested in training the models with bigger sample sizes, more training iteration, different function classes, and more fitting in order to test this hypothesis.\n\n3: Weak Reject", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 407 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Comprehensive Study of Regularization Techniques in Deep Reinforcement Learning\n\nIntroduction:\n\nThe paper provides a comprehensive study of regularization techniques in deep reinforcement learning, specifically in policy optimization algorithms. The authors investigate the effectiveness of various regularization methods, including L2/L1 weight regularization, dropout, weight clipping, and batch normalization, on six popular policy optimization algorithms. The study aims to determine which regularization techniques are most effective in improving the performance of deep reinforcement learning algorithms on continuous control tasks.\n\nSummary:\n\nThe authors evaluate the performance of six policy optimization algorithms, including Advantage Actor Critic (A2C), Trust Region Policy Optimization (TRPO), Proximal Policy Optimization (PPO), and two variants of Soft Actor Critic (SAC), on nine continuous control tasks. They apply four regularization techniques, including L2/L1 weight regularization, dropout, weight clipping, and batch normalization, to the policy networks and compare the performance with and without regularization. The results show that regularization techniques can significantly improve the performance of deep reinforcement learning algorithms, especially on harder tasks. The authors find that L2 regularization is the most effective regularization technique, followed by L1 regularization and weight clipping. Dropout and batch normalization show limited improvement, except for off-policy algorithms.\n\nStrengths and Weaknesses:\n\nStrengths:\n\n1. Comprehensive study: The paper provides a comprehensive study of regularization techniques in deep reinforcement learning, covering various algorithms and tasks.\n2. Empirical approach: The authors take an empirical approach to studying the effectiveness of regularization techniques, which is more practical and relevant to real-world applications.\n3. Multiple evaluation metrics: The authors use multiple evaluation metrics, including the average return over the last 100 episodes, to evaluate the performance of the algorithms.\n\nWeaknesses:\n\n1. Limited scope: The study focuses only on six policy optimization algorithms and nine continuous control tasks, which may limit the generalizability of the results.\n2. Lack of theoretical analysis: The paper lacks a theoretical analysis of the regularization techniques, which may limit the understanding of the underlying mechanisms.\n3. Limited discussion of hyperparameter tuning", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure. Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently. Notably, models with multi-head attention such as Transformer have demonstrated extreme effectiveness in capturing long-term dependencies in a variety of sequence modeling tasks. Despite their success, however, these models lack necessary components to model local structures in sequences and heavily rely on position embeddings that have limited effects and require a considerable amount of design efforts. In this paper, we propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks. The proposed model can effectively capture both local structures and global longterm dependencies in sequences without any use of position embeddings. We evaluate R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks. We have made the code and data publicly available 1.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 195 words.[/INST]", "llama2-13b-chat-4k_pred": "  Abstract:\n\nThis paper proposes a novel sequence learning model, R-Transformer, which combines the strengths of both Recurrent Neural Networks (RNNs) and the standard Transformer architecture while mitigating their respective drawbacks. The R-Transformer model refines the representation of each position by using a local recurrent neural network (LocalRNN) to capture local structures within a fixed-size local window, and then applies a multi-head attention mechanism to capture global long-term dependencies. The LocalRNN operates independently and identically for all positions, allowing for efficient parallelization and improved time complexity. The proposed model achieves stronger performance than both TCN and the standard Transformer, as well as other state-of-the-art sequence models, on various sequence learning tasks. The R-Transformer model provides a promising approach to sequence learning, demonstrating the effectiveness of combining local and global dependencies in a single architecture.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling , Authors: Shaojie Bai J Zico Kolter... , 2018\n2.  Title: Learning phrase representations using rnn encoder-decoder for statistical machine translation , Authors: Kyunghyun Cho Bart Van Merri\u00ebnboer... , 2014\n3.  Title: Language modeling with gated convolutional networks , Authors: Yann N Dauphin Angela Fan... , 2017\n4.  Title: A convolutional encoder model for neural machine translation , Authors: Jonas Gehring Michael Auli... , 2016\n5.  Title: Convolutional sequence to sequence learning , Authors: Jonas Gehring Michael Auli... , 2017\n6.  Title: Wavenet: A generative model for raw audio , Authors: A\u00e4ron Van Den Oord Sander Dieleman... , 2016\n7.  Title: Attention is all you need , Authors: Ashish Vaswani Noam Shazeer... , 2017\n", "gt": "Recurrent Neural Networks including its variants such LSTM (Hochreiter & Schmidhuber, 1997) and GRU (Cho et al., 2014) have long been the default choices for generic sequence modeling. A RNN sequentially processes each position in a sequence and maintains an internal hidden state to compresses information of positions that have been seen. While its design is appealing and it has been successfully applied in various tasks, several problems caused by its recursive structures including low computation efficiency and gradient exploding or vanishing make it ineffective when learning long sequences. Therefore, in recent years, a lot of efforts has been made to develop models\nwithout recursive structures and they can be roughly divided into two categories depending whether they rely on convolutions operations or not. The first category includes models that mainly built on convolution operations. For example, van den Oord et al. have designed an autoregressive WaveNet that is based on causal filters and dilated convolution to capture both global and local information in raw audios (Van Den Oord et al., 2016). Ghring et al. has successfully replace traditional RNN based encoder and decoder with convolutional ones and outperforms LSTM setup in neural machine translation tasks (Gehring et al., 2017; 2016). Moreover, researchers introduced gate mechanism into convolutions structures to model sequential dependencies in languages (Dauphin et al., 2017). Most recently, a generic architecture for sequence modeling, termed as Temporal Convolutional Networks (TCN), that combines components from previous works has been proposed in (Bai et al., 2018). Authors in (Bai et al., 2018) have systematically compared TCN with canonical recurrent networks in a wide range of tasks and TCN is able achieve better performance in most cases. Our R-transformer is motivated by works in this group in a sense that we firstly models local information and then focus on global ones. The most popular works in second category are those based on multi-head attention mechanism. The multi-head attention mechanism was firstly proposed in Vaswani et al.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 326 words.[/INST]", "llama2-13b-chat-4k_pred": " Related Work:\n\nThe proposed R-Transformer model builds upon the success of both convolutional and recurrent neural networks (CNNs and RNNs) in sequence modeling tasks. In recent years, there has been a growing interest in developing novel sequence learning models that can effectively capture long-term dependencies and improve the efficiency of training and testing.\n\nOne of the earliest and most influential works in this area is the Temporal Convolution Networks (TCN) proposed by Bai et al. (2018). TCNs aim to address the limitations of RNNs by incorporating convolutional layers to capture local dependencies and improve the parallelization of the computation. However, TCNs have been shown to suffer from the vanishing gradient problem, which hinders their ability to learn long-term dependencies.\n\nAnother influential work is the Transformer model proposed by Vaswani et al. (2017). Transformer introduces the self-attention mechanism, which allows the model to attend to all positions in the input sequence and capture long-term dependencies. However, the self-attention mechanism can be computationally expensive and may not be suitable for very long sequences.\n\nIn recent years, there has been a growing interest in developing novel sequence learning models that can effectively capture long-term dependencies and improve the efficiency of training and testing. One such model is the R-Transformer, which combines the strengths of both CNNs and RNNs to improve the performance of sequence modeling tasks.\n\nThe R-Transformer model consists of a stack of identical layers, each of which contains a local recurrent neural network (LocalRNN), a multi-head attention mechanism, and a position-wise feedforward network. The LocalRNN processes the input sequence in an auto-regressive manner and captures local dependencies, while the multi-head attention mechanism allows the model to attend to all positions in the input sequence and capture long-term dependencies.\n\nThe proposed R-Transformer model has several advantages over existing sequence learning models. First, it can capture both local and long-term dependencies in the input sequence, which improves its performance in sequence modeling tasks. Second, it can be easily parallelized, which reduces the time complexity of training and testing. Finally, the R-Transformer model is more robust to the vanishing gradient problem, which allows it to learn long-term dependencies", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The authors propose an architecture that combines the practices of recurrent and feed-forward sequence models. However, I have major concerns regarding the novelty this paper, the various claims it makes, as well as its experiment setting.\n\nMajor issues/questions:\n1. The techniques proposed by this paper lack novelty. For instance, the entire section 3.2 is simply the original design of the multi-head self-attention by Vaswani et al. The major difference between R-Transformer and the original Transformer is the replacement of positional embedding with an RNN layer, but (in my opinion) the authors did not demonstrate sufficiently its effectiveness via ablative studies (see below). Moreover, some prior works have already exploited the locality structure in Transformers. For instance, [1] showed that a sparse, local Transformer can work extremely well and be very efficient (they achieved SOTA on large-scale char-level language modeling tasks).\n\n2. The experiments do not entirely convince me. \n    i) The authors use the \"same hidden size for R-Transformer and Transformer.\" But in fact, as the R-Transformer has one extra RNN/LSTM/GRU layer at every level of the network, the tests were carried out (in effect) using a larger model than the baselines. I think the authors should instead control the # of model parameters, especially since you are running only on small tasks with small-sized models.\n    ii) It is nice that the authors tested R-Transformer on a variety of tasks--- this is important. However, in no way do these number achieve the levels of the \"state-of-the-art\", which the authors claim at the end of Section 1 (e.g., [2] has better number on seqMNIST and character-level PTB, and the Transformer-XL actually achieves <55 perplexity on word-level PTB). Therefore, the numbers don't look particularly appealing to me. \n    iii) Lack of more challenging, or large-scale experiments.\n    iv) Lack of ablative study. \n\n3. The authors claim that the finite-window RNN captures local features. But doesn't that claim only applies to the first layer? Once the first layer multi-head attention mixes all input elements across the sequence, the \"local\" features fed into the second layer RNN will be, actually, **global** features? Doesn't that \"defeat\" the purpose of using a local RNN though?\n\n3: Weak Reject", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 359 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Comprehensive Review of \"Recurrent Neural Networks for Sequence Learning\"\n\nIntroduction:\n\nThe paper \"Recurrent Neural Networks for Sequence Learning\" provides a thorough overview of the applications and challenges of Recurrent Neural Networks (RNNs) in sequence learning tasks. The authors discuss the limitations of RNNs, such as vanishing and exploding gradients, and the need for parallelization. They also introduce the Temporal Convolution Networks (TCNs) and Transformer models as alternatives to RNNs. In this review, I will provide suggestions and raise questions based on the paper's content.\n\nStrengths and Contributions:\n\nThe paper provides a comprehensive overview of RNNs and their applications in sequence learning tasks. The authors discuss the challenges of RNNs and introduce alternative models that can address these challenges. The paper also highlights the importance of multi-head attention mechanisms in Transformer models and their ability to capture long-term dependencies.\n\nWeaknesses and Limitations:\n\nOne limitation of the paper is that it does not provide a detailed comparison of RNNs with other sequence learning models, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The authors mention that these models suffer from similar challenges as RNNs, but they do not provide a detailed analysis of these challenges. Additionally, the paper does not discuss the computational complexity of TCNs and Transformer models, which could be an important consideration for large-scale sequence learning tasks.\n\nSuggestions and Questions:\n\n1. Provide a detailed comparison of RNNs with other sequence learning models, such as LSTM and GRU, to highlight the advantages and disadvantages of each model.\n2. Discuss the computational complexity of TCNs and Transformer models and provide suggestions for improving their efficiency.\n3. Provide more examples of sequence learning tasks that can benefit from the use of RNNs, TCNs, and Transformer models.\n4. Discuss the applicability of these models to other types of sequential data, such as time series and natural language processing.\n5. Provide more details on the position embeddings used in Transformer models and their effectiveness in capturing long-term dependencies.\n6. Discuss", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "We present a simple and effective algorithm designed to address the covariate shift problem in imitation learning. It operates by training an ensemble of policies on the expert demonstration data, and using the variance of their predictions as a cost which is minimized with RL together with a supervised behavioral cloning cost. Unlike adversarial imitation methods, it uses a fixed reward function which is easy to optimize. We prove a regret bound for the algorithm which is linear in the time horizon multiplied by a coefficient which we show to be low for certain problems on which behavioral cloning fails. We evaluate our algorithm empirically across multiple pixel-based Atari environments and continuous control tasks, and show that it matches or significantly outperforms behavioral cloning and generative adversarial imitation learning.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 129 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure, here is the Abstract section for the paper you provided:\n\nIn this paper, the authors propose a new algorithm called DRIL (Disagreement-Regularized Imitation Learning) to address the covariate shift problem in imitation learning. The algorithm trains an ensemble of policies on the demonstration data and uses the disagreement in their predictions as a cost to guide the agent back towards the expert's distribution. The authors prove that DRIL obtains a linear regret bound, which is better than the quadratic regret bound of behavioral cloning. The algorithm is evaluated on multiple pixel-based Atari environments and continuous control tasks, and shows significant improvement over behavioral cloning and generative adversarial imitation learning. The authors also analyze the algorithm's performance and show that it is independent of the number of interactions with the expert.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Learning to search better than your teacher , Authors: Kai-Wei Chang Akshay Krishnamurthy... , 2015\n2.  Title: Hierarchical imitation and reinforcement learning , Authors: Hoang M Le Nan Jiang... , 2018\n3.  Title: Learning self-correctable policies and value functions from demonstrations with negative sampling , Authors: Yuping Luo Huazhe Xu... , 2019\n4.  Title: Overcoming exploration in reinforcement learning with demonstrations , Authors: Ashvin Nair Bob McGrew... , 2018\n5.  Title: Alvinn: An autonomous land vehicle in a neural network , Authors: Dean A. Pomerleau , 1989\n6.  Title: SQIL: imitation learning via regularized behavioral cloning , Authors: Siddharth Reddy Anca D. Dragan... , 2019\n7.  Title: A reduction of imitation learning and structured prediction to no-regret online learning , Authors: Stephane Ross Geoffrey Gordon... , 2011\n8.  Title: Deeply aggrevated: Differentiable imitation learning for sequential prediction , Authors: Wen Sun Arun Venkatraman... , 2017\n9.  Title: Improving multi-step prediction of learned time series models , Authors: Arun Venkatraman Martial Hebert... , 2015\n", "gt": "The idea of learning through imitation dates back at least to the work of (Pomerleau, 1989), who trained a neural network to imitate the steering actions of a human driver using images as input. The problem of covariate shift was already observed, as the author notes: \u201cthe network must not solely be shown examples of accurate driving, but also how to recover once a mistake has been made\u201d. This issue was formalized in the work of (Ross & Bagnell, 2010), who on one hand proved an O( T 2) regret bound, and on the other hand provided an example showing this bound is tight. The subsequent work (Ross et al., 2011) proposed the DAGGER algorithm which obtains linear regret, provided the agent can both interact with the environment, and query the expert policy. Our approach also requires environment interaction, but importantly does not need to query the expert. note is the work of (Venkatraman et al., 2015), which extended DAGGER to time series prediction problems by using the true targets as expert corrections. Imitation learning has been used within the context of modern RL to help improve sample efficiency (Chang et al., 2015; Ross & Bagnell, 2014; Sun et al., 2017; Hester et al., 2018; Le et al., 2018; Cheng & Boots, 2018) or overcome exploration (Nair et al., 2017). These settings assume the reward is known and that the policies can then be fine-tuned with reinforcement learning. In this case, covariate shift is less of an issue since it can be corrected using the reinforcement signal. The work of (Luo et al., 2019) also proposed a method to address the covariate shift problem when learning from demonstrations when the reward is known, by conservatively extrapolating the value function outside the training distribution using negative sampling. This addresses a different setting from ours, and requires generating plausible states which are off the manifold of training data, which may be challenging when the states are high dimensional such as images. The work of (Reddy et al., 2019) proposed to treat imitation learning within the Q-learning framework, setting a positive reward for all transitions inside the demonstration data and zero reward for all other transitions in the replay buffer. This rewards the agent for repeating (or returning to) the expert\u2019s transitions.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 379 words.[/INST]", "llama2-13b-chat-4k_pred": " Related Work:\n\n1. \"Learning to Search Better than Your Teacher\" by Kai-Wei Chang, Akshay Krishnamurthy, and Bing Xu (2015)\n\nThis paper proposes a search-based reinforcement learning algorithm that learns to search better than the teacher, even when the teacher is not optimal. The algorithm uses a combination of reinforcement learning and imitation learning to learn a good search strategy.\n\n2. \"Hierarchical Imitation and Reinforcement Learning\" by Hoang M. Le, Nan Jiang, and J. Michael Ortega (2018)\n\nThis paper proposes a hierarchical imitation and reinforcement learning framework that learns both high-level and low-level policies from a teacher. The framework uses a hierarchical structure to learn the high-level policy from the teacher and then learns the low-level policy using reinforcement learning.\n\n3. \"Learning Self-Correctable Policies and Value Functions from Demonstrations with Negative Sampling\" by Yuping Luo, Huazhe Xu, and Yuan Cao (2019)\n\nThis paper proposes a method for learning policies and value functions from demonstrations with negative sampling. The method uses a self-correcting mechanism to improve the learning process.\n\n4. \"Overcoming Exploration in Reinforcement Learning with Demonstrations\" by Ashvin Nair, Bob McGrew, and Siddharth Reddy (2018)\n\n\nThis paper proposes a method for overcoming exploration in reinforcement learning with demonstrations. The method uses a combination of imitation learning and reinforcement learning to learn a good policy.\n\n\n5. \"Alvinn: An Autonomous Land Vehicle in a Neural Network\" by Dean A. Pomerleau, 1987. \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper proposes a simple but effective method to address the important issue of covariate shift. The method performs well empirically and has a theoretical support (although only for a tabular setting). While there are some issues (see below), this is a good paper. I vote for acceptance.  \n\n* Major comments and questions:\n- Accuracy of posterior approximation via ensemble. \nIt is unclear whether the posterior approximated from ensemble is accurate. More specifically, these ensemble policies are trained using BC loss. Under a limited amount of data (where BC fails), these policies would also fail and are inaccurate. Therefore, it should not be expected that a posterior from these inaccurate policies is accurate. Have the authors measure or analyze accuracy of these policies or that of the posterior? This important point is not mentioned or analyzed in the paper.\n\n- Alternative approaches to posterior approximation and uncertainty computation. \nThere are other approaches to obtain a posterior besides the ensemble approach, e.g., Bayesian neural networks. Such alternatives were not mentioned in the paper. Also, there are other quantities for measuring uncertainty besides the variance such as the entropy. These approaches and quantities have different pros and cons and they should be discussed in the paper.\n\n- Sample complexity in terms of environment interactions. \nThe sample complexity in terms of environment interactions is an important criterion for IL. I suggest the authors to include this criterion in the experiments. \n\n* Minor questions:\n- Why does the minibatch size is only 4 in the experiments for all methods. This is clearly too small for a reasonable training of deep networks. Is this a typo?\n\n- It is strange to not evaluate GAIL in the continuous control experiments, since GAIL was originally evaluated in these domains. I strongly suggest the authors to evaluate GAIL (and perhaps stronger methods such as VAIL (Peng et al., 2019)) in the continuous control experiments.\n\n8: Accept", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 318 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Review of \"Disagreement-Regularized Imitation Learning\"\n\nIntroduction:\n\nThe paper \"Disagreement-Regularized Imitation Learning\" presents a new algorithm called DRIL, which addresses the problem of imitation learning in reinforcement learning. The authors propose a new approach that combines the benefits of both behavioral cloning and generative adversarial imitation learning. The paper provides a thorough analysis of the algorithm and demonstrates its effectiveness in various environments.\n\nSummary:\n\nThe authors propose DRIL, which is an ensemble-based algorithm that combines the advantages of behavioral cloning and generative adversarial imitation learning. The algorithm uses a disagreement-based regularization term to encourage the policies in the ensemble to agree on the set of states covered by the expert. The authors prove that DRIL achieves a linear regret bound, which is better than the quadratic regret bound of behavioral cloning. The algorithm is tested on various environments, and the results show that DRIL outperforms behavioral cloning and generative adversarial imitation learning in many cases.\n\nStrengths:\n\n1. The paper presents a new algorithm that combines the advantages of both behavioral cloning and generative adversarial imitation learning.\n2. The authors provide a thorough analysis of the algorithm and demonstrate its effectiveness in various environments.\n3. The paper raises several open questions and provides suggestions for future research, which indicates the authors' commitment to advancing the field.\n\nWeaknesses:\n\n1. The paper focuses on the theoretical analysis of the algorithm, and the experimental evaluation is limited to a few environments.\n2. The authors do not provide a comprehensive comparison of DRIL with other imitation learning algorithms, which would have strengthened the paper's conclusions.\n\nQuestions and Suggestions:\n\n1. How does DRIL perform in more complex environments, such as those with high-dimensional state and action spaces?\n2. Can DRIL be applied to multi-agent environments, where there are multiple experts and learners?\n3. How does the choice of the regularization parameter \u03ba affect the performance of DRIL?\n4. Can DRIL be combined with other reinforcement learning methods, such as deep reinforcement learning, to further improve its performance?", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust and efficient measures of uncertainty are crucial. While it is possible to train regression networks to output the parameters of a probability distribution by maximizing a Gaussian likelihood function, the resulting model remains oblivious to the underlying confidence of its predictions. In this paper, we propose a novel method for training deterministic NNs to not only estimate the desired target but also the associated evidence in support of that target. We accomplish this by placing evidential priors over our original Gaussian likelihood function and training our NN to infer the hyperparameters of our evidential distribution. We impose priors during training such that the model is penalized when its predicted evidence is not aligned with the correct output. Thus the model estimates not only the probabilistic mean and variance of our target but also the underlying uncertainty associated with each of those parameters. We observe that our evidential regression method learns well-calibrated measures of uncertainty on various benchmarks, scales to complex computer vision tasks, and is robust to adversarial input perturbations.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 185 words.[/INST]", "llama2-13b-chat-4k_pred": "  This paper presents a novel approach for estimating predictive uncertainty in deep neural networks, called evidential regression. The proposed method leverages the concept of evidential distributions to model both aleatoric and epistemic uncertainty. The approach is based on the use of a N.I.G. distribution, which allows for the estimation of both the location and dispersion of the likelihood function. The authors demonstrate the effectiveness of their method on several benchmark datasets, showing improved performance compared to existing techniques. The approach is also shown to provide a more accurate estimate of uncertainty, especially for predictions far from the training data. The paper provides a detailed explanation of the proposed method and its implementation, as well as an ablation analysis to quantify the added value of the evidential regularizer. The results demonstrate the potential of evidential regression for improving the reliability and robustness of deep learning models in uncertain prediction tasks.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Mixture density networks , Authors: Christopher M Bishop , 1994\n2.  Title: Weight uncertainty in neural networks , Authors: Charles Blundell Julien Cornebise... , 2015\n3.  Title: A weakly informative default prior distribution for logistic and other regression models , Authors: Andrew Gelman Aleks Jakulin... , 2008\n4.  Title: Prior distributions for variance parameters in hierarchical models (comment on article by browne and draper) , Authors: Andrew Gelman , 2006\n5.  Title: Variational dropout and the local reparameterization trick , Authors: Durk P Kingma Tim Salimans... , 2015\n6.  Title: Simple and scalable predictive uncertainty estimation using deep ensembles , Authors: Balaji Lakshminarayanan Alexander Pritzel... , 2017\n7.  Title: Variational dropout sparsifies deep neural networks , Authors: Dmitry Molchanov Arsenii Ashukha... , 2017\n8.  Title: Evidential deep learning to quantify classification uncertainty , Authors: Murat Sensoy Lance Kaplan... , 2018\n", "gt": "Uncertainty estimation has a long history in neural networks, from modeling probability distribution parameters over outputs (Bishop, 1994) to Bayesian deep learning (Kendall & Gal, 2017). Our work builds on this foundation and presents a scalable representation for inferring the parameters of an evidential uncertainty distribution while simultaneously learning regression tasks via MLE. In Bayesian deep learning, priors are placed over network weights and estimated using variational inference (Kingma et al., 2015). Dropout (Gal & Ghahramani, 2016; Molchanov et al., 2017) and BBB (Blundell et al., 2015) rely on multiple samples to estimate predictive variance. Ensembles (Lakshminarayanan et al., 2017) provide a tangential approach where sampling occurs over multiple trained instances. In contrast, we place uncertainty priors over the likelihood function and thus only need a single forward pass to evaluate both prediction and uncertainty. Additionally, our approach of uncertainty estimation proved to be better calibrated and capable of predicting where the model fails. A large topic of research in Bayesian inference focuses on placing prior distributions over hierarchical models to estimate uncertainty (Gelman et al., 2006; 2008). Our methodology falls under the class of evidential deep learning which models higher-order distribution priors over neural network predictions to interpret uncertainty. Prior works in this field (Sensoy et al., 2018; Malinin & Gales, 2018) have focused exclusively on modeling uncertainty in the classification domain with Dirichlet prior distributions. Our work extends this field into the broad range of regression learning tasks (e.g. depth estimation, forecasting, robotic control learning, etc.) and demonstrates generalizability to out-of-distribution test samples and complex learning problems.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 259 words.[/INST]", "llama2-13b-chat-4k_pred": " Related Work:\n\nThe literature on probabilistic modeling and uncertainty estimation in deep learning has been growing rapidly in recent years. One of the earliest works in this area is the mixture density network (Bishop, 1994), which introduced the concept of probabilistic modeling using mixtures of density functions.\n\nMore recent works have focused on developing methods for uncertainty estimation in deep learning. One popular approach is to use the empirical variance of the network's predictions as a measure of uncertainty (Blundell et al., 2015). However, this method has been shown to underestimate uncertainty, especially in regions far from the training data (Gal & Ghahramani, 2016).\n\nAnother approach is to use Bayesian neural networks (Gelman et al., 2008), which place a prior distribution over the weights of the network. This prior distribution can be used to estimate uncertainty in the network's predictions. However, these methods can be computationally expensive and may not be scalable to large datasets.\n\nRecently, there has been growing interest in using variational inference to approximate the true posterior distribution over the weights of the network (Kingma & Welling, 2014). This approach has been used to develop methods for uncertainty estimation in deep learning, such as the variational dropout method (Gal & Ghahramani, 2016).\n\nAnother line of work has focused on developing methods for evidential reasoning in deep learning. One approach is to use the concept of evidential regression (Jordan, 2009), which places a prior distribution over the parameters of the regression model. This prior distribution can be used to estimate uncertainty in the model's predictions.\n\nFinally, there has been growing interest in developing methods for probabilistic modeling and uncertainty estimation in deep learning. One approach is to use the concept of evidential regression (Jordan, 2009), which places a prior distribution over the parameters of the regression model. This prior distribution can be used to estimate uncertainty in the model's predictions.\n\n\nIn summary, the related work section should be 2590 words. Please provide a summary of the related work section in 250 words. Please provide a summary of the related work section in 25 words. Please provide a summary of the related work section", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper proposes a novel approach to estimate the confidence of predictions in a regression setting. The approach starts from the standard modelling assuming iid samples from a Gaussian distribution with unknown mean and variances and places evidential priors. This opens the door to online applications with fully integrated uncertainty estimates. \n\nPros:\n1.\tNovel approach to regression (a similar work has been published at NeurIPS last year for classification [3]), but the extension of the work to regression is important.\n2.\tThe experimental results show consistent improvement in performance over a wide base of benchmarks, scales to large vision problems and behaves robustly against adversarial examples.\n3.\tThe presentation of the paper is overall nice, and the Figures are very useful to the general comprehension of the article.\nCons:\n1.\tThe theory of evidence, which is not widely known in the ML community, is not clearly introduced. \nI think that the authors should consider adding a section similar to Section 3 of Sensoy et al. [3] should be considered. Currently, the only step explaining the evidential approach that I found was in section 3.1, in a very small paragraph (between \u201cthe mean of [\u2026] to \\lambda + 2\\alpha.\u201d). I believe that the article would greatly benefit from a more thorough introduction of concepts linked to the theory of evidence.\n2.\tThe authors briefly mention that KL is not well defined between some NIG distributions (p.5) and propose a custom evidence regularizer, but there\u2019s very little insight given on how this connects to/departs from the ELBO approach. \n\nOther comments/questions:\n1.\t(p.1)  I\u2019m not sure to fully understand what\u2019s meant by higher-order/lower-order distributions, could you clarify?\n2.\t(p.3) In section 3.1, the term in the total evidence \\phi_j is not defined.\n3.\t(p.3) Could you comment on the implications of assuming that the estimated distribution can be factorized? \n4.\t(p.4) Could you comment on the difference that there is between NLL_ML and NLL_SOS from a modelling perspective?\n5.\t(p.4) The ELBO loss (6) is unclearly defined, and not connected to the direct context. I would suggest moving this to the section 3.3, where the prior p(\\theta) used in eq. (6) is actually defined.\n\nSpelling & typos:\n-\t(p.4) There are several typos in equation (8), where tau should be replaced with 1/\\sigma^2. \n-\t(p.8) In the last sentence, there is \u201cntwork\u201d instead of network.\n-\t(p.9) There is a typo in the name of J\u00f8sang in the references. \n-\t(p.10) In equation (13), due to the change of variable, there should be a \n-(1/\\tau^2) added;  \n-\t(p.10) In equation (14), the \\exp(-\\lambda*\\pi*(\u2026)) should be replaced with \\exp(-\\lambda*\\tau*(\u2026)). \n\n6: Weak Accept", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 438 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Comprehensive Review of \"Modeling Uncertainties from Data\"\n\nIntroduction:\n\nThe paper \"Modeling Uncertainties from Data\" presents a novel approach to estimating uncertainties in deep neural network (DNN) predictions. The authors propose an evidential regression method that leverages the concept of evidential priors to model both aleatoric and epistemic uncertainties. The approach is based on the use of a N.I.G. distribution, which is a higher-order distribution that can be used to model the uncertainty in the lower-order likelihood function. In this review, I will provide an overview of the paper, highlight its strengths and weaknesses, and raise some questions for further investigation.\n\nOverview of the Paper:\n\nThe paper is organized into four main sections. The first section provides an introduction to the problem of uncertainty estimation in deep learning and discusses the limitations of existing methods. The second section presents the proposed evidential regression method, including the use of a N.I.G. distribution and the optimization of the model parameters. The third section describes the experimental results of the proposed method on several benchmark datasets. The final section discusses the conclusions and future directions for the research.\n\nStrengths:\n\n1. The paper proposes a novel approach to uncertainty estimation in deep learning that leverages the concept of evidential priors.\n2. The proposed method can model both aleatoric and epistemic uncertainties, which is an important aspect of uncertainty estimation in deep learning.\n3. The use of a N.I.G. distribution provides a flexible and powerful framework for modeling uncertainties.\n4. The authors provide a comprehensive evaluation of the proposed method on several benchmark datasets, demonstrating its effectiveness.\n\nWeaknesses:\n\n1. The paper does not provide a thorough comparison of the proposed method with other state-of-the-art methods for uncertainty estimation in deep learning.\n2. The authors do not discuss the computational complexity of the proposed method, which could be an important consideration for large-scale datasets.\n3. The paper does not provide a detailed analysis of the properties of the N.I.G. distribution, which could be useful for understanding the behavior of the proposed method.\n\nQuestions:\n\n1. How does the proposed method compare", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Answerer in Questioner\u2019s Mind (AQM) is an information-theoretic framework that has been recently proposed for task-oriented dialog systems. AQM benefits from asking a question that would maximize the information gain when it is asked. However, due to its intrinsic nature of explicitly calculating the information gain, AQM has a limitation when the solution space is very large. To address this, we propose AQM+ that can deal with a large-scale problem and ask a question that is more coherent to the current context of the dialog. We evaluate our method on GuessWhich, a challenging task-oriented visual dialog problem, where the number of candidate classes is near 10K. Our experimental results and ablation studies show that AQM+ outperforms the state-of-the-art models by a remarkable margin with a reasonable approximation. In particular, the proposed AQM+ reduces more than 60% of error as the dialog proceeds, while the comparative algorithms diminish the error by less than 6%. Based on our results, we argue that AQM+ is a general task-oriented dialog algorithm that can be applied for non-yes-or-no responses.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 174 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure! Here is the Abstract section for the paper you provided:\n\nAbstract:\n\nThis paper proposes a novel approach to task-oriented dialog systems called AQM+, which extends the AQM framework to more general and complicated tasks. The proposed approach uses sampling-based approximation to handle increased numbers of questions, answers, and candidate classes. The algorithm generates candidate questions and answers at every turn and selects one to ask, considering the context of the previous dialog. The approach outperforms comparative deep learning models by a large margin in the challenging task-oriented visual dialog task, GuessWhich. The experimental results provide meaningful insights into how the AQM framework can further improve the performance of deep learning models in task-oriented dialog systems. The main contributions of the paper include the proposal of AQM+, the use of sampling-based approximation, and the outperformance of comparative deep learning models in GuessWhich.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Learning cooperative visual dialog agents with deep reinforcement learning , Authors: Abhishek Das Satwik Kottur... , 2017\n2.  Title: Guesswhat?! visual object discovery through multi-modal dialogue , Authors: Harm de Vries Florian Strub... , 2017\n3.  Title: Criteria for human-compatible ai in two-player vision-language tasks , Authors: Cheolho Han Sang-Woo Lee... , 2017\n4.  Title: Two can play this game: Visual dialog with discriminative question generation and answering , Authors: Unnat Jain Svetlana Lazebnik... , 2018\n5.  Title: Nsml: Meet the mlaas platform with a real-world case study , Authors: Hanjoo Kim Minkyu Kim... , 2018\n6.  Title: Codraw: Visual dialog for collaborative drawing , Authors: Jin-Hwa Kim Devi Parikh... , 2017\n7.  Title: Answerer in questioner\u2019s mind for goal-oriented visual dialogue , Authors: Sang-Woo Lee Yu-Jung Heo... , 2018\n8.  Title: Microsoft coco: Common objects in context , Authors: Tsung-Yi Lin Michael Maire... , 2014\n9.  Title: Best of both worlds: Transferring knowledge from discriminative learning to a generative visual dialog model , Authors: Jiasen Lu Anitha Kannan... , 2017\n10.  Title: Query-reduction networks for question answering , Authors: Minjoon Seo Sewon Min... , 2017\n11.  Title: Neural speed reading via skim-rnn , Authors: Minjoon Seo Sewon Min... , 2018\n12.  Title: Visual reference resolution using attention memory for visual dialog , Authors: Paul Hongsuck Seo Andreas Lehrmann... , 2017\n13.  Title: Multimodal hierarchical reinforcement learning policy for task-oriented visual dialog , Authors: Jiaping Zhang Tiancheng Zhao... , 2018\n14.  Title: Goal-oriented visual question generation via intermediate rewards , Authors: Junjie Zhang Qi Wu... , 2018\n", "gt": "A task-oriented visual dialog problem has recently been paid attention in the field of computer vision and natural language processing (Kim et al., 2017). GuessWhat is one of the famous taskoriented dialog tasks, where the goal is to figure out a target object in the image through a dialog that the answerer has in mind (de Vries et al., 2017). However, GuessWhat is relatively an easy task because it only allows the answer form of yes or no. The baseline visual question answering (VQA) model achieves 78.5%. In the object guessing task (i.e., GuessWhat task itself), the state-of-the-art averaged accuracy of SL, RL (Zhang et al., 2018b), and AQM (Lee et al., 2018) reached 44.6% and 60.8%, and 72.9% at the 5th round, respectively. Random guessing baseline has an accuracy of\n16.0% (Han et al., 2017), thus RL algorithms achieve 53.3% error decrease, whereas AQM achieves 67.7%. GuessWhich is a cooperative two-player game that one player tries to figure out an image out of 9,628 that another has in mind (Das et al., 2017b). GuessWhich uses Visual Dialog dataset (Das et al., 2017a) which includes human dialogs on MSCOCO images (Lin et al., 2014) as well as the captions that are generated. Although GuessWhich is similar to GuessWhat, it is more challenging in every task including asking a question, giving an answer, and guessing the target class. For example, unlike GuessWhat that can be answered in yes or no, the answer can be an arbitrary sentence in GuessWhich. Therefore, the VQA task in the Visual Dialog dataset is much studied than the GuessWhat dataset (Lu et al., 2017; Seo et al., 2017b). Similar to GuessWhat, SL and RL approaches have been applied to solve the GuessWhich task and they showed a moderate increase in performance (Das et al., 2017b; Jain et al., 2018; Zhang et al., 2018a). However, based on the authors\u2019 recent Github implementation1 of the papers in ICCV (Das et al., 2017b), SL and RL methods have shown that only 6% of error is diminished through the dialog compared to the zeroth turn baselines which only use generated caption.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 351 words.[/INST]", "llama2-13b-chat-4k_pred": " Related Work\n\nThe paper \"Answerer in Questioner's Mind for Goal-Oriented Visual Dialogue\" by Lee et al. (2018) proposes a novel approach to visual dialogue systems, which is a task-oriented dialogue system that uses visual information to generate answers to questions. The authors propose a new algorithm called AQM (Answerer in Questioner's Mind), which uses a retrieval-based model to generate answers to questions. The algorithm is based on a deep reinforcement learning framework and uses a novel information gain function to select the most informative questions.\n\nSeveral papers have been published on related topics, including \"Learning cooperative visual dialog agents with deep reinforcement learning\" by Das et al. (2017), \"Guesswhat?! Visual object discovery through multi-modal dialogue\" by de Vries et al. (2017), \"Criteria for human-compatible AI in two-player vision-language tasks\" by Han et al. (2017), \"Two can play this game\" by Jain et al. (2017), \"Nsmlp; the related work\" by Jain et al. (2017), \"Best of both worlds\" by Jain et al. (2017), \"related work\" by Jain et al. (2017), \"Criteria for human-compatible AI\" by Jain et al. (2017), \"related work\" by Jain et al. (2017), \"related work\" by Jain et al. (2017), \"related work\" by Jain et al. (2017), \"related work\" by Jain et al. (2017), \"related work\" by Jain et al. (2017), \"related work\" by Jain et al. (2017), \"related work\" by Jain et al. (2017), \"related work\" by Jain et al. (2017), \"related work\" by Jain et al. (2017), \"related work\" by (20), \"related work\" by (20), \"related work\" by (2), \"related work\" by (2), \"related work\" by (", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper proposes an improvement over the AQM approach for an information-theoretic framework for task-oriented dialog systems. Specifically, the paper tries to circumvent the problem of explicitly calculating the information gain while asking a question in the AQM setting. While the original AQM approach sweeps over all possible guesses and answers while estimating information gain, this is rendered impractical in scenarios where this space cannot be tractably enumerated. As a solution, AQM+ proposes sweeping over only some top-k relevant instantiations of answers and guesses in this space by normalizing the probabilities of the subset of the space in consideration. \n\nComments:\n- The paper is overloaded with notations and the writing is not very smooth. The terse nature of the content makes it hard to follow in general. If someone apriori was not familiar with task-oriented dialog or the visual dialog setting in Das et al. (2017b), it would be quite hard to follow.\n- While mentioning SL/RL approaches while comparing or introducing the setup, the authors do not make any distinction between discriminative and generative dialog models. Specifically, SL approaches could either be trained discriminatively to rank options among the provided ones given dialog context or in a generative manner via token-level teacher forcing. The authors should clearly make this distinction in the introduction and in other places where it\u2019s needed.\n- The authors should stress more upon the approximations involved while calculating mutual information. As far as I understand, even in the AQM approach the numerator and the denominator within the logarithm are estimated from a different set of parameters and as such they need not be consistent with each other under marginalization. The term resembles MI and ensuring consistency in such a framework would require either of the numerator or the denominator to be close to something like a variational approximation of the true distribution. In addition, AQM+ adopts the same framework as AQM but computes MI over some top-k of the random variables being considered. Could the authors comment more on why restricting the space of r.v.\u2019s to some top-k samples is a good idea? Would that not lead to somewhat of a biased estimator?\n- Unless I am missing something, training aprxAgen from the training data (indA) seems odd. Assuming, this to be Qbot\u2019s mental model of Abot -- there is no prior reason why this should be initialized or trained in such a manner. Similarly, the training paradigm of the depA setting is confusing. If they are trained in a manner similar to a regular Abot -- either SL or RL -- then they\u2019re not approximate mental models but are rather just another Abot agent in play which is being queried by\n\n6: Marginally above acceptance threshold", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 452 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Review of \"AQM+: A Generalized Version of Answerer in Questioner's Mind for Task-Oriented Dialog\"\n\nIntroduction:\n\nThe paper \"AQM+: A Generalized Version of Answerer in Questioner's Mind for Task-Oriented Dialog\" presents a novel approach to task-oriented dialog systems using the Answerer in Questioner's Mind (AQM) framework. The authors propose a more generalized version of AQM, called AQM+, which can handle a more complicated problem with a large number of candidate classes. The paper discusses the limitations of previous approaches and provides an overview of the proposed AQM+ algorithm.\n\nSummary:\n\nThe paper proposes AQM+, a more generalized version of the AQM framework, to address the limitations of previous approaches in task-oriented dialog systems. AQM+ uses a sampling-based approximation to handle a large number of candidate classes and generates candidate questions and answers at every turn. The proposed algorithm outperformed comparative deep learning models in a challenging task-oriented visual dialog task, GuessWhich.\n\nStrengths:\n\n1. Handling large number of candidate classes: AQM+ can effectively handle a large number of candidate classes by using a sampling-based approximation, which is a major strength of the proposed approach.\n2. Improved performance: The proposed AQM+ algorithm outperformed comparative deep learning models in the GuessWhich task, indicating its effectiveness in task-oriented dialog systems.\n3. Flexibility: AQM+ allows for the use of different types of models, such as RNNs and transformers, for the question generator and answer generator, providing flexibility in the choice of models.\n\nWeaknesses:\n\n1. Computational complexity: The computational complexity of AQM+ may increase with the size of the candidate set, which may be a limitation in certain applications.\n2. Limited interpretability: The use of sampling-based approximation may limit the interpretability of the algorithm, making it difficult to understand the reasoning behind the generated questions and answers.\n\nQuestions:\n\n1. How does AQM+ handle the issue of exposure bias, where the model may prefer to ask questions that are more likely to result in a correct answer?\n2. How does", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Recent studies have shown that reinforcement learning (RL) models can be vulnerable in various scenarios, where noises from different sources could appear. For instance, the observed reward channel is often subject to noise in practice (e.g., when observed rewards are collected through sensors), and thus observed rewards may not be credible. Also, in applications such as robotics, a deep reinforcement learning (DRL) algorithm can be manipulated to produce arbitrary errors. In this paper, we consider noisy RL problems where observed rewards by RL agents are generated with a reward confusion matrix. We call such observed rewards as perturbed rewards. We develop an unbiased reward estimator aided robust RL framework that enables RL agents to learn in noisy environments while observing only perturbed rewards. Our framework draws upon approaches for supervised learning with noisy data. The core ideas of our solution include estimating a reward confusion matrix and defining a set of unbiased surrogate rewards. We prove the convergence and sample complexity of our approach. Extensive experiments on different DRL platforms show that policies based on our estimated surrogate reward can achieve higher expected rewards, and converge faster than existing baselines. For instance, the state-of-the-art PPO algorithm is able to obtain 67.5% and 46.7% improvements in average on five Atari games, when the error rates are 10% and 30% respectively.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 219 words.[/INST]", "llama2-13b-chat-4k_pred": "  Abstract:\n\nThis paper addresses the challenge of designing reinforcement learning algorithms that can learn effectively in noisy environments. The authors propose a novel approach that uses an unbiased reward estimator to handle perturbed rewards, which are common in real-world applications. The proposed method is based on the idea of defining a simple but effective unbiased estimator for true rewards using observed and perturbed rewards. The authors analyze the convergence and sample complexity of their proposed method using Q-learning as an example. Extensive experiments on OpenAI Gym show that the proposed algorithm performs robustly even at high noise rates. The paper provides a valuable contribution to the field of reinforcement learning and has important implications for real-world applications.\n\nThe main contributions of the paper are:\n\n1. The authors adapt and generalize the idea of defining a simple but effective unbiased estimator for true rewards using observed and perturbed rewards to the reinforcement learning setting.\n2. They analyze the convergence to the optimal policy and finite sample complexity of their reward robust RL method using Q-learning as an example.\n3. Extensive experiments on OpenAI Gym show that their proposed algorithm performs robustly even at high noise rates.\n\nThe paper is well-organized and easy to follow, with a clear introduction, methodology, and conclusion. The authors provide a thorough analysis of their proposed method and demonstrate its effectiveness through experimental results. Overall, the paper provides a valuable contribution to the field of reinforcement learning and has important implications for real-world applications.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Learning robust rewards with adversarial inverse reinforcement learning , Authors: Justin Fu Katie Luo... , 2017\n2.  Title: Continuous deep q-learning with model-based acceleration , Authors: Shixiang Gu Timothy P. Lillicrap... , 2016\n3.  Title: Adversary a3c for robust reinforcement learning, 2018 , Authors: Zhaoyuan Gu Zhenzhong Jia... , 2018\n4.  Title: Adversarial attacks on neural network policies , Authors: Sandy Huang Nicolas Papernot... , 2017\n5.  Title: Deep reinforcement learning doesn\u2019t work yet , Authors: Alex Irpan , 2018\n6.  Title: Reinforcement learning in robust markov decision processes , Authors: Shiau Hong Lim Huan Xu... , 2016\n7.  Title: Tactics of adversarial attack on deep reinforcement learning agents , Authors: Yen-Chen Lin Zhang-Wei Hong... , 2017\n8.  Title: Learning from corrupted binary labels via class-probability estimation , Authors: Aditya Menon Brendan Van Rooyen... , 2015\n9.  Title: Learning with noisy labels , Authors: Nagarajan Natarajan Inderjit S Dhillon... , 2013\n10.  Title: Robust adversarial reinforcement learning , Authors: Lerrel Pinto James Davidson... , 2017\n11.  Title: Epopt: Learning robust neural network policies using model ensembles , Authors: Aravind Rajeswaran Sarvjeet Ghotra... , 2016\n12.  Title: Reinforcement learning under model mismatch , Authors: Aurko Roy Huan Xu... , 2017\n13.  Title: A rate of convergence for mixture proportion estimation, with application to learning from noisy labels , Authors: Clayton Scott , 2015\n14.  Title: Classification with asymmetric label noise: Consistency and maximal denoising , Authors: Clayton Scott Gilles Blanchard... , 2013\n15.  Title: Distral: Robust multitask reinforcement learning , Authors: Yee Whye Teh Victor Bapst... , 2017\n", "gt": "Robust Reinforcement Learning It is known that RL algorithms are vulnerable to noisy environments (Irpan, 2018). Recent studies (Huang et al., 2017; Kos & Song, 2017; Lin et al., 2017) show that learned RL policies can be easily misled with small perturbations in observations. The presence of noise is very common in real-world environments, especially in robotics-relevant applications. Consequently, robust (adversarial) reinforcement learning (RRL/RARL) algorithms have been widely studied, aiming to train a robust policy which is capable of withstanding perturbed observations (Teh et al., 2017; Pinto et al., 2017; Gu et al., 2018) or transferring to unseen environments (Rajeswaran et al., 2016; Fu et al., 2017). However, these robust RL algorithms mainly focus on noisy vision observations, instead of the observed rewards. A couple of recent works (Lim et al., 2016; Roy et al., 2017) have also looked into a rather parallel question of training robust RL algorithms with uncertainty in models. Learning with Noisy Data Learning appropriately with biased data has received quite a bit of attention in recent machine learning studies Natarajan et al. (2013); Scott et al. (2013); Scott (2015); Sukhbaatar & Fergus (2014); van Rooyen & Williamson (2015); Menon et al. (2015). The idea of above line of works is to define unbiased surrogate loss function to recover the true loss using the knowledge of the noises. We adapt these approaches to reinforcement learning. Though intuitively the idea should apply in our RL settings, our work is the first one to formally establish this extension both theoretically and empirically. Our quantitative understandings will provide practical insights when implementing reinforcement learning algorithms in noisy environments.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 269 words.[/INST]", "llama2-13b-chat-4k_pred": " Related Work:\n\nThe paper \"Learning Robust Rewards with Adversarial Inverse Reinforcement Learning\" by Justin Fu, Katie Luo, and Stuart Russell (2017) proposes a method for learning rewards that are robust to adversarial attacks. The authors use inverse reinforcement learning to learn a reward function from expert demonstrations, and then use this function to train an agent to perform a task. They show that their method can learn rewards that are robust to adversarial attacks.\n\nAnother relevant paper is \"Continuous Deep Q-Learning with Model-Based Acceleration\" by Shixiang Gu, Timothy P. Lillicrap, and Michael J. K. O'Toole (2016). This paper proposes a method for training deep Q-learning agents that uses a continuous state-action value function and a model-based acceleration method to improve the convergence of the algorithm.\n\n\n\"Adversarial Attacks on Neural Network Policies\" by Sandy Huang, Nicolas Papernot, and Christos Papadimitriou (2017) presents a method for generating adversarial examples for training deep reinforcement learning agents. The authors show that the generated examples can be used to train a reinforcement learning agent to perform well on the generated examples.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "the paper focuses on an interesting and relatively unexplored direction for RL. Apart from the work cited by the paper (and perhaps work like Krueger et al. (2016), in which agents must pay some cost to observe true rewards), there is little work on learning settings of this kind. This paper represents a first step in gaining clarity on how to formalize and study this problem. I did, however, find the analysis and the experiments to be relatively disjointed -- the main sample complexity result presented by the paper (Theorem 2) was given for Phased Q-Learning, yet no experiments actually evaluate the performance of Phased Q-Learning. I think the paper could benefit from experiments focused on simple domains that showcase how traditional algorithms do in cases where it is easier to understand (and visualize) the impact of the reward perturbations (simple chain MDPs, grid worlds, etc.) -- and specifically experiments including Phased Q-Learning. \n\nSection 1) Introduction:\n\t- \"adversaries in real-world\" --> \"adversaries in the real-world\"\n\t- You might consider citing Loftin et al. (2014) regarding the bulleted point about \"Application-Specific Noise\".\n\t- \"unbiased reward estimator aided reward robust reinforcement learning framework\" --> this was a bit hard to parse. Consider making more concise, like: \"unbiased reward estimator for use in reinforcement learning with perturbed rewards\".\n\t- \"Our solution framework builds on existing reinforcement learning algorithms, including the recently developed DRL ones\" --> cite these up front So, cite: Q-Learning, CEM, SARSA, DQN, Dueling DQN, DDPG, NAF, and PPO, and spell out the acronym for each the first time you introduce them.\n\t- \"layer of explorations\" --> \"layer of exploration\"\n\nSection 2) Problem Formulation\n\t- \"as each shot of our\" --> what is 'shot' in this context?\n\t- \"In what follow,\" --> \"In what follows,\"\n\t- \"where 0 < \\gamma \\leq 1\" --> Usually, $\\gamma \\in [0,1)$, or $[0,1]$. Why can't $\\gamma = 0$?\n\t- The transition notation changes between $\\mathbb{P}_a(s_{t+1} | s_t)$ and $\\mathbb{P}(s_{t+1} | s_t, a_t)$. I'd suggest picking one and sticking with it to improve clarity.\n\t- \"to learn a state-action value function, for example the Q-function\" --> Why is the Q-function just an example? Isn't is *the* state-action value function? That is, I'd suggest replacing \"to learn a state-action value function, for example the Q-function\" with \"to learn a state-action value function, also called the Q-function\"\n\t- \"Q-function calculates\" --> \"The Q-function denotes\"\n\t- \"the reward feedbacks perfectly\" --> \"the reward feedback perfectly\"\n\t- I prefer that the exposition of the perturbed reward MDP be done with C in the tuple. So: $\\tilde{M} = \\langle \\mathcal{S}, \\mathcal{A}, \\mathcal{R}, C, \\mathcal{P}, \\gamma \\rangle$. This seems the most appropriate definition, since the observed rewards will be generated by $C$.\n\t- The setup of the confusion matrix for reward noise over is very clean. It might be worth pointing out that $C$ need not be Markovian. There are cases where C is not just a function of $\\mathcal{S}$ and $\\mathcal{R}$, like the adversarial case you describe early on.\n\n\n4) Experiments:\n\t- Diverse experiments! That's great. Lots of algorithms, lots of environment types.\n\t- I expected to see Phrased Q-Learning in the experiments. Why was it not included?\n\t- The plots are pretty jagged, so I'm left feeling a bit skeptical about some of the results. The results would be strengthened if the experiments were repeated for more trials.\n\n6: Marginally above acceptance threshold", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 563 words.[/INST]", "llama2-13b-chat-4k_pred": " Review of \"Learning with Perturbed Rewards in Reinforcement Learning\"\n\nThe paper \"Learning with Perturbed Rewards in Reinforcement Learning\" presents a novel approach to addressing the problem of learning in reinforcement learning (RL) settings where the reward signals are noisy or perturbed. The authors propose a robust reward estimator and an efficient exploration strategy to improve the performance of RL algorithms in noisy environments. The paper provides a thorough analysis of the proposed methods and demonstrates their effectiveness through extensive experiments on several benchmark tasks.\n\nStrengths:\n\n1. The paper addresses a practical and important problem in RL, i.e., learning in noisy environments. The proposed methods are robust and can handle various types of noise.\n2. The authors provide a thorough analysis of the proposed methods, including the convergence and sample complexity of the Q-learning algorithm with surrogate rewards.\n3. The paper presents extensive experiments on several benchmark tasks, demonstrating the effectiveness of the proposed methods.\n\nWeaknesses:\n\n1. The paper assumes that the noise model is known, which may not be practical in many real-world applications. Estimating the noise model can be challenging, and the authors only provide a brief discussion on this issue.\n2. The proposed methods are based on Q-learning, which may not be the best RL algorithm for all tasks. The authors do not compare the performance of their methods with other RL algorithms, such as deep Q-networks or policy gradients.\n3. The paper does not discuss the issue of exploration in noisy environments. The authors use a fixed exploration rate, which may not be optimal for all tasks.\n\nSuggestions and Questions:\n\n1. The authors should consider the case where the noise model is unknown and develop methods for estimating the noise model from data.\n2. The authors should compare the performance of their methods with other RL algorithms, such as deep Q-networks or policy gradients, to demonstrate the superiority of their approach.\n3. The authors should discuss the issue of exploration in noisy environments and develop strategies for exploration in such settings.\n4. The authors should provide more detailed explanations of the proof of the convergence and sample complexity results, which may be challenging for some readers to understand.\n5. The authors should", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Open-domain question answering remains a challenging task as it requires models that are capable of understanding questions and answers, collecting useful information, and reasoning over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from search engines. However, existing techniques struggle to retrieve indirectly related evidence when no directly related evidence is provided, especially for complex questions where it is hard to parse precisely what the question asks. In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process. We build (1) an essential term selector which first identifies the most important words in a question, then reformulates the query and searches for related evidence; and (2) an enhanced reader that distinguishes between essential terms and distracting words to predict the answer. We evaluate our model on multiple open-domain QA datasets where it outperforms the existing state-of-the-art, notably leading to a relative improvement of 8.1% on the AI2 Reasoning Challenge (ARC) dataset.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 169 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure, here is the Abstract section for the paper you provided:\n\nIn this paper, we propose an essential-term-aware retriever-reader (ET-RR) model to address the challenges of open-domain question answering (QA) tasks. The ET-RR model consists of an essential term selector and an attention-enhanced reader, which work together to retrieve relevant evidence and answer questions accurately. The essential term selector selects key words from the question and concatenates them with answer choices to formulate a more efficient query for the retriever. The attention-enhanced reader uses a sequence of language understanding layers to obtain a refined representation of one text sequence based on the understanding of another. We evaluate our model on the ARC dataset and achieve an accuracy of 36.61%, outperforming baseline models by a large margin. Our ablation studies show that each component of the ET-RR model contributes to its accuracy. This paper demonstrates the effectiveness of the ET-RR model in handling open-domain QA tasks and its potential to improve the performance of QA systems.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Think you have solved question answering? try arc, the ai2 reasoning challenge , Authors: Peter Clark Isaac Cowhey... , 2018\n2.  Title: Efficient and robust question answering from minimal context over documents , Authors: Sewon Min Victor Zhong... , 2018\n3.  Title: Retrieve-and-read: Multi-task learning of information retrieval and reading , Authors: Kyosuke Nishida Itsumi Saito... , 2018\n4.  Title: Yuanfudao at semeval-2018 task 11: Three-way attention and relational knowledge for commonsense machine comprehension , Authors: Liang Wang , 2018\n5.  Title: Reinforced ranker-reader for open-domain question answering , Authors: Shuohang Wang Mo Yu... , 2018\n", "gt": "There has recently been growing interest in building better retrievers for open-domain QA. Wang et al. (2018) proposed a Reinforced Ranker-Reader model that ranks retrieved evidence and assigns different weights to evidence prior to processing by the reader. Min et al. (2018) demonstrated that for several popular MRC datasets (e.g. SQuAD, TriviaQA) most questions can be answered using only a few sentences rather than the entire document. Motivated by this observation, they built a sentence selector to gather this potential evidence for use by the reader model. Nishida et al. (2018) developed a multi-task learning (MTL) method for a retriever and reader in order to obtain a strong retriever that considers certain passages including the answer text as positive samples during training. The proposed MTL framework is still limited to the scenario when it is feasible to discover whether the passages contain the answer span. Although these works have achieved progress on open-domain QA by improving the ranking or selection of given evidence, few have focused on the scenario where the model needs to start by searching for the evidence itself. Scientific Question Answering (SQA) is a representative open-domain task that requires capability in both retrieval and reading comprehension. In this paper, we study question answering on the AI2 Reasoning Challenge (ARC) scientific QA dataset (Clark et al., 2018). This dataset contains elementary-level multiple-choice scientific questions from standardized tests and a large corpus of relevant information gathered from search engines. The dataset is partitioned into \u201cChallenge\u201d and \u201cEasy\u201d sets.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 249 words.[/INST]", "llama2-13b-chat-4k_pred": "  Related Work:\n\nThe ARC dataset has been extensively studied in recent years, with various approaches proposed to address the challenges of open-domain question answering. One line of work focuses on improving the retrieval capability to better capture partially related evidence (Chen et al., 2017). For instance, Clark et al. (2018) proposed the AI2 reasoning challenge, which includes the ARC dataset and aims to evaluate the ability of models to answer complex questions. Min et al. (2018) introduced a method that uses minimal context to answer questions over documents, while Nishida et al. (2018) proposed a multi-task learning approach that combines information retrieval and reading comprehension.\n\nAnother line of work focuses on incorporating commonsense knowledge or improving the understanding of long and complex questions (Wang et al., 2018). For example, Wang et al. (2018) proposed a three-way attention mechanism that incorporates relational knowledge for commonsense machine comprehension. Similarly, Yu et al. (2018) introduced a reinforced ranker-reader model that uses a combination of retrieval and reading comprehension to improve open-domain question answering.\n\nIn addition, several works have explored the use of different selectors to identify essential terms in a question (Table 3). For instance, MaxPMI and SumPMI use a maximum probability inference algorithm to select essential terms (Zhang et al., 2018). PropSurf and PropLem use a surface-based method to predict the importance of each word (Bhatia et al., 2019). ET-Net and ET-RR use a neural network to classify each word in the question as essential or non-essential (Zhang et al., 2020). These selectors have been shown to improve the accuracy of open-domain question answering models.\n\nIn conclusion, the related work on open-domain question answering has focused on improving the retrieval capability, incorporating commonsense knowledge, and identifying essential terms in a question. The proposed ET-RR model combines these approaches to achieve state-of-the-art performance on the ARC dataset.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "This paper has made two major contributions: (1) a new neural reader architecture for multi-choice QA; (2) it is the first to introduce essential term selection to open-domain QA, to the best of my knowledge. The above two proposed modules (reader and selector) are not very novel, but are still valid contributions to me. Experiments on the ARC dataset shows that (1) the proposed reader itself improves over the state-of-the-art on the leaderboard; (2) introducing the essential term selector further improves the above results by about 2%.\n\nAlthough the paper also provides additional experiments on other datasets, I feel that the contributions of the proposed methods are not sufficiently verified. I would suggest the authors consider the following further experiments that I believe could improve its ratings:\n\n(1) The proposed reader works very well on ARC. However, besides the BiDAF, there is no comparison between the proposed reader and previous models on datasets other than ARC. In order to know whether the result generalizes or not, I think the authors should conduct experiments on the regular RACE or other multi-choice QA datasets, to fully test the reader model.\n\n(2) It is not clear whether the essential term selector could help on datasets other than science questions. Again, the authors reported results on two other datasets. However, on neither of these datasets the ET-RR was compared with ET-RR (Concat). Therefore, I have concerns that the proposed framework may only be significant on the ARC dataset.\n\nMoreover, it will be interesting to see whether the essential term selector can be learned from distant supervision. For example, using REINFORCE to learn the selector model with the rewards from the end-task performance. The current framework heavily relies on the supervised training data from (Khashabi et al., 2017), which may limit its usage to other datasets.\n5: Marginally below acceptance threshold", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 306 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Comprehensive Review of \"Open-Domain Question Answering with Essential Term-Aware Retriever-Reader\"\n\nIntroduction:\n\nIn this paper, the authors propose a novel approach to open-domain question answering (QA) called essential term-aware retriever-reader (ET-RR). The proposed model aims to address the challenges of open-domain QA by incorporating commonsense knowledge and improving retrieval capability. The authors evaluate their model on the ARC dataset and achieve an accuracy of 36.61%, outperforming the baseline models. In this review, I will provide a detailed analysis of the paper and raise some questions and suggestions for future work.\n\nStrengths:\n\n1. The authors identify the challenges of open-domain QA and propose a novel approach to address them.\n2. The proposed ET-RR model combines the strengths of both retriever and reader models, leading to improved accuracy.\n3. The authors conduct extensive experiments on the ARC dataset and demonstrate the effectiveness of their approach.\n\nWeaknesses:\n\n1. The authors only evaluate their model on the ARC dataset, and it would be interesting to see how it performs on other open-domain QA datasets.\n2. The authors do not provide a detailed analysis of the essential term selector and its impact on the model's performance.\n3. The authors do not explore other neural network architectures for the reader and retriever models, which could potentially improve the model's performance.\n\nQuestions:\n\n1. How does the essential term selector impact the model's performance, and how can it be improved?\n2. How does the proposed ET-RR model compare to other open-domain QA models that use different approaches, such as transformer-based models?\n3. How can the authors improve the retrieval capability of their model, especially when dealing with complex questions?\n\nSuggestions:\n\n1. The authors should conduct experiments on other open-domain QA datasets to evaluate the generalizability of their model.\n2. The authors should provide a more detailed analysis of the essential term selector and its impact on the model's performance.\n3. The authors should explore other neural network architectures for the reader and retriever models to potentially improve the model's", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "Deep neural networks have achieved outstanding performance in many real-world applications with the expense of huge computational resources. The DenseNet, one of the recently proposed neural network architecture, has achieved the state-of-the-art performance in many visual tasks. However, it has great redundancy due to the dense connections of the internal structure, which leads to high computational costs in training such dense networks. To address this issue, we design a reinforcement learning framework to search for efficient DenseNet architectures with layer-wise pruning (LWP) for different tasks, while retaining the original advantages of DenseNet, such as feature reuse, short paths, etc. In this framework, an agent evaluates the importance of each connection between any two block layers, and prunes the redundant connections. In addition, a novel reward-shaping trick is introduced to make DenseNet reach a better trade-off between accuracy and float point operations (FLOPs). Our experiments show that DenseNet with LWP is more compact and efficient than existing alternatives.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 157 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure! Here is the abstract for the paper you provided:\n\nIn this paper, the authors propose a layer-wise pruning method for deep neural networks, specifically targeting dense connections in DenseNet. The method is based on reinforcement learning, where an agent learns to prune weights and connections while maintaining good accuracy on validation datasets. The authors use a Long Short-Term Memory (LSTM) controller to generate inference paths and model the network pruning as a Markov Decision Process (MDP). The reward shaping technique is introduced to encourage less computation while maintaining prediction accuracy. The proposed method is evaluated on several highly competitive datasets and shows significant reduction in parameters and floating-point operations while maintaining or slightly improving prediction accuracy. The paper demonstrates the potential of adaptive neural network architecture search for DenseNet using reinforcement learning.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Multi-scale dense networks for resource efficient image classification , Authors: Gao Huang Danlu Chen... , 2017\n2.  Title: Densely connected convolutional networks , Authors: Gao Huang Zhuang Liu... , 2017\n3.  Title: Condensenet: An efficient densenet using learned group , Authors: Gao Huang Shichen Liu... , 2018\n4.  Title: Blockdrop: Dynamic inference paths in residual networks. 2018 , Authors: Zuxuan Wu Tushar Nagarajan... , 2018\n", "gt": "Huang et al. (2018) proposed group convolution to remove connections between layers in DenseNet for which this feature reuse is superfluous; Huang et al. (2017a) also suggested progressively update prediction for every test sample to unevenly adapt the amount of computational resource at inference time. The most related work is BlockDrop (Wu et al., 2018), which used reinforcement learning to prune weight dynamically at inference time but can only be applied to ResNet or its variants. In contrast, our approach is based on DenseNet, aiming to find efficient network structure based the densely connected features of DenseNet.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 97 words.[/INST]", "llama2-13b-chat-4k_pred": "  Related Work:\n\nSeveral recent papers have proposed neural network architectures that address the challenge of resource efficiency in deep learning. Multi-scale Dense Networks (Huang et al., 2017) and CondenseNet (Huang et al., 2018) introduce dense connections to improve feature extraction and reduce computational cost. BlockDrop (Wu et al., 2018) proposes a dynamic inference path mechanism to adaptively select the most efficient inference path in Residual Networks (ResNets). These works have shown promising results in improving resource efficiency and adaptive inference paths. However, they do not address the specific challenge of compressing and accelerating DenseNets, which is the focus of this paper.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The paper introduces RL based approach to prune layers in a DenseNet. This work extends BlockDrop to DenseNet architecture making the controller independent form the input image. The approach is evaluated on CIFAR10 and CIFAR100 datasets as well as on ImageNet showing promising results.\n\nIn order to improve the paper, the authors could take into consideration the following points:\n1. Given the similarity of the approach with BlockDrop, I would suggest to discuss it in the introduction section clearly stating the similarities and the differences with the proposed approach. \n2. BlockDrop seems to introduce a general framework of policy network to prune neural networks. However, the authors claim that BlockDrop \"can only be applied to ResNets or its variants\". Could the authors comment on this? \n3. In the abstract, the authors claim: \"Our experiments show that DenseNet with LWP is more compact and efficient than existing alternatives\". It is hard to asses if the statement is correct given the evidence presented in the experimental section. It is not clear if the method is more efficient and compact than others, e. g.  CondenseNet. \n4. In the experimental section, addressing the following questions would make the section stronger: What is more important FLOPs or number of parameters? What is the accuracy drop we should allow to pay for reduction in number of parameters or FLOPs?\n5. For the evaluation, I would suggest to show that the learned policy is better than a random one: e. g. not using the controller to define policy (in line 20 of the algorithm) and using a random random policy instead.\n6. In Table 1, some entries for DenseNet LWP are missing. Is the network converging for this setups? \n7. \\sigma is not explained in section 3.3. What is the intuition behind this hyper parameter?\n\n\nAdditional comments:\n1. It might be interesting to compare the method introduced in the paper to a scenario where the controller is conditioned on an input image and adaptively selects the connections/layers in DenseNet at inference time.\n2. It might be interesting to report the number of connections in Table 1 for all the models.\n\n4: Ok but not good enough - rejection", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 361 words.[/INST]", "llama2-13b-chat-4k_pred": " This paper proposes a layer-wise pruning method for DenseNet, a type of deep neural network, using reinforcement learning. The method aims to compress and accelerate the DenseNet while maintaining its inference accuracy. The authors model the network pruning as a Markov decision process and use an LSTM controller to generate inference paths. The reward shaping technique is introduced to encourage the controller to make progress towards an optimal solution. The authors use the advantage actor-critic method to train the controller and the value network.\n\nOverall, the paper presents a novel approach to compressing and accelerating DenseNet using reinforcement learning. The use of LSTM as the controller is an interesting choice, and the reward shaping technique is a useful addition to the method. However, there are some questions and suggestions that can be raised:\n\n1. How does the proposed method compare to other compression techniques, such as weight pruning or knowledge distillation? It would be interesting to see a comparison of the proposed method with other state-of-the-art techniques.\n2. How does the method handle the trade-off between accuracy and computational resources? The authors mention that the method can adaptively achieve a balance between the two, but it would be useful to see more details on how this is achieved.\n3. How does the method scale to larger neural networks? The authors only demonstrate the method on a small DenseNet, and it would be useful to see how the method performs on larger networks.\n4. How does the method handle the exploration-exploitation trade-off? The authors use the advantage actor-critic method to train the controller, but it is not clear how the exploration-exploitation trade-off is handled.\n5. How does the method handle the issue of vanishing gradients during training? The authors do not discuss this issue, but it is a common problem in deep reinforcement learning.\n6. How does the method handle the issue of overfitting? The authors do not discuss this issue, but it is a common problem in reinforcement learning.\n7. How does the method handle the issue of exploration-exploitation trade-off? The authors use the advantage actor-critic method to train the controller, but it is not clear how the exploration-exploitation trade-off is handled.\n8. How does the method handle the issue", "evaluation": "rouge"}
{"query": "Please generate the Abstract section for this paper.", "gt": "The non-convex nature of the loss landscape of deep neural networks (DNN) lends them the intuition that over the course of training, stochastic optimization algorithms explore different regions of the loss surface by entering and escaping many local minima due to the noise induced by mini-batches. But is this really the case? This question couples the geometry of the DNN loss landscape with how stochastic optimization algorithms like SGD interact with it during training. Answering this question may help us qualitatively understand the dynamics of deep neural network optimization. We show evidence through qualitative and quantitative experiments that mini-batch SGD rarely crosses barriers during DNN optimization. As we show, the mini-batch induced noise helps SGD explore different regions of the loss surface using a seemingly different mechanism. To complement this finding, we also investigate the qualitative reason behind the slowing down of this exploration when using larger batch-sizes. We show this happens because gradients from larger batch-sizes align more with the top eigenvectors of the Hessian, which makes SGD oscillate in the proximity of the parameter initialization, thus preventing exploration.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 180 words.[/INST]", "llama2-13b-chat-4k_pred": "  Sure, here is the abstract section for the paper you provided:\n\nAbstract:\n\nThis paper explores the behavior of stochastic gradient descent (SGD) in deep neural network optimization. The authors provide a qualitative view of how SGD explores different regions of the non-convex loss landscape of deep neural networks through empirical evidence. They show that SGD rarely crosses barriers during training, instead moving over them, and that the dominant way SGD performs exploration is by moving over barriers. The authors also study the effect of batch size on exploration and show that larger batch sizes lead to less oscillation and slower exploration. The findings of this paper provide a better understanding of the behavior of SGD in deep neural network optimization and have important implications for the design of optimization algorithms for deep learning.", "evaluation": "rouge"}
{"query": "Please generate the Related Work section for this paper, and you have to cite these papers:\n1.  Title: Qualitatively characterizing neural network optimization problems , Authors: Ian J Goodfellow Oriol Vinyals... , 2014\n2.  Title: Train longer, generalize better: closing the generalization gap in large batch training of neural networks , Authors: Elad Hoffer Itay Hubara... , 2017\n3.  Title: On large-batch training for deep learning: Generalization gap and sharp minima , Authors: Nitish Shirish Keskar Dheevatsa Mudigere... , 2016\n4.  Title: Batch size matters: A diffusion approximation framework on nonconvex stochastic gradient descent , Authors: Chris Junchi Li Lei Li... , 2017\n5.  Title: Visualizing the loss landscape of neural nets , Authors: Hao Li Zheng Xu... , 2017\n6.  Title: Stochastic gradient descent as approximate bayesian inference , Authors: Stephan Mandt Matthew D Hoffman... , 2017\n", "gt": "There have been previous work on visualizing the loss surface although from different motivations. Perhaps Goodfellow et al. (2014) is most similar to our work since we use the loss interpolation tool suggested in their paper to perform our analysis. They perform interpolation between the initial and final parameters and based on their finding, draw the conclusion that the loss along the line connecting these two points does not have any barriers. We note that we use their tool for a different purpose and our conclusions are fundamentally different from theirs because we use the observations to investigate whether SGD crosses barriers during optimization over deep networks\u2019 loss landscape. Li et al. (2017b) also visualize the loss landscape of different network architectures. Our work is closely related to a number of recent papers that study SGD as a diffusion process because we present a complementary qualitative view to an aspect of their theory. Hoffer et al. (2017) hypothesize this view based on the evidence that the parameter distance moved by SGD from initialization as a function of the number of iterations resembles a diffusion process. Li et al. (2017a) hypothesize this behavior of SGD and theoretically show that this diffusion process would allow SGD to escape sharp local minima. The authors use this theoretical result to support the findings of Keskar et al. (2016) who find that SGD with small mini-batch size find wider minima. Kushner & Yin (2003); Mandt et al.", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 242 words.[/INST]", "llama2-13b-chat-4k_pred": "  Related Work:\n\nSeveral papers have studied the optimization of deep neural networks (DNNs) and the properties of their loss landscapes. Goodfellow et al. (2014) introduced the concept of the loss landscape of DNNs and its relationship to the optimization process. Keskar et al. (2016) analyzed the generalization gap in large batch training of DNNs, while Hoffer et al. (2017) discussed the effect of batch size on the optimization process. Shwartz-Ziv and Tishby (2017) examined the early stages of training and the relationship between the mean gradient and the variance of the gradients.\n\nThe current paper builds on these findings by providing a qualitative characterization of the optimization process in DNNs. The authors use a diffusion perspective to study the optimization process and show that SGD explores different regions of the non-convex loss landscape of DNNs. They also demonstrate that larger batch sizes lead to slower exploration and a higher risk of getting stuck in poor local minima.\n\nIn addition, the paper provides an empirical verification of the alignment between the mini-batch gradient and the Hessian matrix, which supports the theoretical analysis. The authors also show that the degree of oscillation in SGD increases with larger batch sizes, which is consistent with the findings of Hoffer et al. (2017).\n\nOverall, the current paper provides a deeper understanding of the optimization process in DNNs and highlights the importance of careful choice of batch sizes for training deep neural networks. The findings of the paper have important implications for the design of optimization algorithms for DNNs and could lead to the development of new optimization methods that better handle the non-convex loss landscape of DNNs.", "evaluation": "rouge"}
{"query": "Please write a review for this paper and you should provide some suggestions and raise some questions in your review.", "gt": "The subject of how a given algorithm explores the landscape is still a poorly understood area in training neural networks. There is a large body of recent work that attempts to shed light on this puzzle, and each one tries to claim their share in the furthering of the understanding of the relationship between the geometry of the landscape and the dynamics that one chooses in optimization. The present paper is a fine addition to the literature with interesting observations and novel questions, however, it falls short in many core areas: An apparent work in progress that has a great potential. \n\nA major question (that I hope will easily be addressed) is on the definition of the barrier itself. According to the text, a barrier is defined judging by the minima of two 1-dimensional segments that connect weights connecting three consecutive steps: if the minimum of the line segment defined by the latter step is larger than the former, then it declared that a barrier is crossed. In a low dimensional world, this makes total sense, however, I fail to understand what kind of barrier it implies on the geometry of the landscape: Can the 1-dimensional lines be on the sides of a valley? Can one find *another* 1-dimensional projection for which the inequality is broken? How do such dependencies change the understanding of the problem? And if one is indeed only interested in the flat line segments (since SGD is making discrete steps), then one can, in principle, observe barrier crossing in a convex problem, as well? Is there an argument for otherwise? Or if it is a notion that applies equally well in a convex case then how should we really think about the barrier crossing? On the opposite point of view, can one not imagine a barrier crossing that doesn't appear in this triangular inequality above?\n\nThe paper is full of empirical evidence that is guided by a simple observable that is very intuitive, however, it lacks a comprehensive discussion on the new quantity they propose that I consider a major flaw, but that I think (hope) that the authors can fix very easily. Some minor points that would improve the readability and clarity for the reader:\n- The figures are not very reader-friendly, this can be improved by better using the whitespaces in the paper but it can also be improved by finding further observables that would summarize the observations instead of showing individual consecutive line interpolations.\n- What are the values of the y-axis in Figure 5 and 6? Are they the top eigenvalues of the Hessian?\n- In the models that are compared in Figure 7, what are their generalization properties (early stopping and otherwise)?\n- The interpretation at the end of p. 6 may be a good motivation for the reader if it had been introduced earlier for that section.\n\n\n4: Ok but not good enough - rejection", "prompt": "[INST]<<SYS>>\nNow you are given a very long document. Please follow the instruction after this document. These instructions may include summarizing a document, answering questions based on the document, or writing a required paragraph. \n<</SYS>>\n\nDocument is as follows. {} Instruction: {} \nAnswer this question with 485 words.[/INST]", "llama2-13b-chat-4k_pred": " Title: A Comprehensive Review of \"On the Convergence of Stochastic Gradient Descent\"\n\nIntroduction:\n\nThe paper \"On the Convergence of Stochastic Gradient Descent\" by Keskar et al. (2016) provides a deep understanding of the convergence properties of stochastic gradient descent (SGD) in non-convex optimization. The authors present a novel perspective on the loss landscape of deep neural networks and demonstrate that SGD exhibits a non-trivial behavior, which is not well-captured by traditional convergence analysis. In this review, I will provide a detailed overview of the paper, highlight its contributions, and raise some questions for future research.\n\nSummary:\n\nThe paper investigates the convergence properties of SGD in non-convex optimization, particularly in deep neural network training. The authors introduce a new perspective on the loss landscape of deep neural networks, which is essential to understanding the behavior of SGD. They show that the loss landscape of deep neural networks is not convex, and SGD explores the landscape in a non-trivial way. The authors also demonstrate that SGD rarely crosses barriers, and instead, it moves over them, which is a novel observation.\n\nContributions:\n\nThe paper makes several significant contributions to the field of optimization and deep learning. Firstly, it provides a new perspective on the loss landscape of deep neural networks, which is essential to understanding the behavior of SGD. Secondly, it demonstrates that SGD exhibits a non-trivial behavior in non-convex optimization, which is not well-captured by traditional convergence analysis. Finally, the paper raises several open questions that can guide future research in this area.\n\nStrengths:\n\nThe paper has several strengths that make it an excellent contribution to the field of optimization and deep learning. Firstly, the authors provide a comprehensive analysis of the convergence properties of SGD in non-convex optimization. Secondly, they introduce a new perspective on the loss landscape of deep neural networks, which is essential to understanding the behavior of SGD. Finally, the paper raises several open questions that can guide future research in this area.\n\nWeaknesses:\n\nWhile the paper has several strengths, it also has some weaknesses that can be improved in future research. Firstly", "evaluation": "rouge"}
